In the future, scholars who lean on digital helpmates are likely to dominate the rest, enriching our literary culture and changing the kinds of questions that can be explored. Those who resist the temptation to unleash the capabilities of machines will have to content themselves with the pleasures afforded by smaller-scale, and fewer, discoveries. While critics and book reviewers may continue to be an essential part of public cultural life, literary theorists who do not embrace AI will be at risk of becoming an exotic species – like the librarians who once used index cards to search for information.

Like many other AI practitioners, I’m a philosophical functionalist: I believe that a cognitive state, such as one derived from reading, should not be defined by what it is made of in terms of hardware or biology, but instead by how it functions, in relation to inputs, outputs and other cognitive states. (Opponents of functionalism include behaviourists – who insist that mental states are nothing other than dispositions to behave in certain ways – and mind-brain identity theorists – who argue that mental states are identical with particular neural states, and are tied to specific biological ‘hardware’.)

AI criticism is also limited by the accuracy of human labellers, who must carry out a close reading of the ‘training’ texts before the AI can kick in. Experiments show that readers tend to take longer to process events that are distant in time or separated by a time shift (such as ‘a day later’).

Even though AI annotation schemes are versatile and expressive, they’re not foolproof. Longer, book-length texts are prohibitively expensive to annotate, so the power of the algorithms is restricted by the quantity of data available for training them.

In most cases, this analysis involves what’s known as ‘supervised’ machine learning, in which algorithms train themselves from collections of texts that a human has laboriously labelled.

Consider the opening sentence of Gabriel García Márquez’s One Hundred Years of Solitude (1967): ‘Many years later, as he faced the firing squad, Colonel Aureliano Buendía was to remember that distant afternoon when his father took him to discover ice.’ The complex way in which Márquez represents the passage of time is a staple of modern fiction. The time corresponding to ‘Many years later’ includes the fateful time of ‘facing’ the firing squad, which in turn is simultaneous with that final ‘remember’-ing, which is years after ‘that distant afternoon’. In a single sentence, Márquez paints a picture of events in the fleeting present, memories of the past and visions for the future.

The so-called ‘cultural’ turn in literary studies since the 1970s, with its debt to postmodern understandings of the relationship between power and narrative, has pushed the field away from such systematic, semi-mechanistic ways of analysing texts. AI remains concerned with formal patterns, but can nonetheless illuminate key aspects of narrative, including time, space, characters and plot.

The ‘structuralist’ strand of literary theory tends to deploy close – sometimes microscopic – readings of a text to see how it functions, almost like a closed system. This is broadly known as a ‘formal’ mode of literary interpretation, in contrast to more historical or contextual ways of reading.

Bazaars are rhetoric, not reality

As in any contract, there’s a balance of power. If the architecture of today’s web is any indication, that balance is skewed toward the designers. Unless we want to keep pinging around like Skinner’s pigeons (or like poor Michael S), it’s worth paying a little more attention to those whom our attention pays.

‘In the past, if you were an alcohol distiller, you could throw up your hands and say, look, I don’t know who’s an alcoholic,’ he said. ‘Today, Facebook knows how much you’re checking Facebook. Twitter knows how much you’re checking Twitter. Gaming companies know how much you’re using their free-to-play games. If these companies wanted to do something, they could.’

sites such as Facebook and Twitter automatically and continuously refresh the page; it’s impossible to get to the bottom of the feed.

At first, if you want to make money, you sell whatever is in the room. Maybe it’s excellent journalism. Maybe it’s a game or a recipe. Maybe it’s an item that will get shipped to someone’s house. In this model, the internet offers a straightforward transactional experience in digital space.Over time, instead of making money from whatever is in each room, companies begin to monetise the doors. They equip them with sensors. Each time you go through one, someone gets paid. Immediately, some people will start adding a lot of new doors. Other people will build rooms that are largely empty, but that function as waystations, designed to get as many people as possible to enter and leave.

But she argues that, when it comes to machine design, it’s not exactly about giving people what they do or do not want. What matters, Schüll says, is ‘the accentuating, accelerating and elaborating that happens between the wanting and the giving’.

Fact-based journalism now competes with false information for our attention while our cities and citizens become both more connected by technology and more divided by ideology and income. The values reflected in lines of code, whether it be at the ATM, when we search on internet or drive a car, are already affecting what we think, what we do and what information we share with those around us.

Video games need to discover what’s special and different about their own medium to break out of their cultural ghetto.

Smalltalk doesn’t have to be pragmatic, because it’s better than its imitators and the things that make it different are also the things that give it an advantage.

Self contained. All of the source code is accessible inside the image. One can change any of it at any time. Every tool is written in Smalltalk. If one doesn't like how something works, one can change it. If one wants to add a feature, one is empowered to. Since everything is open, one can even change how the language works. It's programming without constraints. The original refactoring tools were written in Smalltalk.

Members can be added to the TC at any time. Any committer can nominate another committer to the TC and the TC uses its standard consensus seeking process to evaluate whether or not to add this new member. Members who do not participate consistently at the level of a majority of the other members are expected to resign.The TC just uses the same consensus seeking process for adding new members as it uses for everything else.It’s a good idea to encourage committers to nominate people to the TC and not just wait around for TC members to notice the impact some people are having. Listening to the broader committers about who they see as having a big impact keeps the TC’s perspective inline with the rest of the project.As a project grows it’s important to add people from a variety of skill sets. If people are doing a lot of docs work, or test work, treat the investment they are making as equally valuable as the hard technical stuff.

Avoid creating big decision hierarchies. Instead, invest in a broad, growing and empowered contributorship that can make progress without intervention. We need to view a constant need for intervention by a few people to make any and every tough decision as the biggest obstacle to healthy Open Source.

In pure consensus everyone essentially has a veto. So, if I don’t want something to happen I’m in a strong position of power over everyone that wants something to happen. They have to convince me and I don’t have to convince anyone else of anything.To avoid this we use a system called “consensus seeking” which has a long history outside of open source. It’s quite simple, just attempt to reach a consensus, if a consensus can’t be reached then call for a majority wins vote.Just the fact that a vote is a possibility means that people can’t be obstructionists, whether someone favor a change or not, they have to convince their peers and if they aren’t willing to put in the work to convince their peers then they probably don’t involve themselves in that decision at all.

Not every committer has the rights to release or make high level decisions, so we can be much more liberal about giving out commit rights. That increases the committer base for code review and bug triage. As a wider range of expertise in the committer pool smaller changes are reviewed and adjusted without the intervention of the more technical contributors, who can spend their time on reviews only they can do.

A key part of the liberal contribution policies we’ve been building is an inversion of the typical code review process. Rather than the default mode for a change to be rejected until enough people sign off, we make the default for every change to land. This puts the onus on reviewers to note exactly what adjustments need to be made in order for it to land.

Sure, we get bad bugs, but we have a ton of contributors who can immediately work with people who log them to educate them on better practices and treat it as an opportunity to educate. This is why we have documentation on writing good bugs, in order to educate contributors, not as a barrier to entry.Creating barriers to entry just reduces the number of people there’s a chance to identify, educate and potentially grow into greater contributors.

We know what happens to unhealthy projects over a long enough time period, more maintainers leave, contributions eventually fall, and if we’re lucky users leave it. When we aren’t so lucky adoption continues and years later we’re plagued with security and stability issues in widely adopt software that can’t be effectively maintained.The number of users a project has is a poor indicator of the health of the project, often it is the most used software that suffers the biggest contribution crisis.

This is what a healthy project should look like. As the demands on the project from increased users rise, so do the contributors, and as contributors increase more are converted into committers. As the committer base grows, more of them rise to the level of expertise where they should be involved in higher level decision making.If these groups don’t grow in proportion to each other they can’t carry the load imposed on them by outward growth. A project’s ability to convert people from each of these groups is the only way it can stay healthy if its user base is growing.

Vocabulary* A Contributor is any individual creating or commenting on an issue or pull request. * A Committer is a subset of contributors who have been given write access to the repository. * A TC (Technical Committee) is a group of committers representing the required technical expertise to resolve rare disputes.

“Funding infrastructure” means more than funding maintainers. This is partially why I think crowdfunding platforms and donation campaigns, while helpful, can only go so far.Instead, we need to take a holistic approach to sustainability: fixing systems, not just throwing money at the same old problems and forcing developers to figure it all out themselves.

Should open source projects look like companies? Of course not. For one thing, a company is a business, and an open source project is a community, with very different goals and needs.

There is little support for functions that directly help code, like documentation or test automation. There is even less support for functions that indirectly help code, like community or evangelism.

In 2012, AKQA’s chief creative officer Rei Inamoto debuted a new wisdom at SXSW: “To run an efficient team, you only need three people: a Hipster, a Hacker, and a Hustler.” The startup trifecta was born.

When we think of open source’s influence on software, we tend to think about the legal implications. Open source made it acceptable to share, use and modify software freely.But open source changed how software companies build proprietary products, too. That open source principles are being used to develop closed source software speaks volumes about its cultural legacy.

Last year, O’Reilly Media went so far as to coin the term InnerSource to characterize this phenomenon:Inspired by the spread of open source software throughout the areas of operating systems, cloud computing, JavaScript frameworks, and elsewhere, a number of companies are mimicking the practices of the powerful open source movement to create an internal company collaboration under the rubric InnerSource.

Git, the version control system released in 2005, made those processes even easier.

The rise of agile coincided with the rise of startups using open source projects. At first, startups just incorporated those projects into their own software. But like any cultural exchange, open source began to affect how startups built products, too.

Corporate practices can be directly hostile to individuals with exceptional skills and initiative in technical matters. I consider such management of technical people cruel and wasteful. Kierkegaard was a strong proponent for the individual against “the crowd” and has some serious discussion of the importance of aesthetics and ethical behavior. I couldn’t point to a specific language feature and say, “See, there’s the influence of the nineteenth-century philosopher,” but he is one of the roots of my reluctance to eliminate “expert level” features, to abolish “misuses,” and to limit features to support only uses that I know to be useful. I’m not particularly fond of Kierkegaard’s religious philosophy, though.

TR: How do you account for the fact that C++ is both widely criticized and resented by many programmers but at the same time very broadly used? Why is it so successful?BS: The glib answer is, There are just two kinds of languages: the ones everybody complains about and the ones nobody uses.There are more useful systems developed in languages deemed awful than in languages praised for being beautiful–many more. The purpose of a programming language is to help build good systems, where “good” can be defined in many ways. My brief definition is, correct, maintainable, and adequately fast. Aesthetics matter, but first and foremost a language must be useful; it must allow real-world programmers to express real-world ideas succinctly and affordably.

TR: In retrospect, in designing C++, wasn’t your decision to trade off programmer efficiency, security, and software reliability for run time performance a fundamental mistake?BS: Well, I don’t think I made such a trade-off. I want elegant and efficient code. Sometimes I get it. These dichotomies (between efficiency versus correctness, efficiency versus programmer time, efficiency versus high-level, et cetera.) are bogus.What I did do was to design C++ as first of all a systems programming language: I wanted to be able to write device drivers, embedded systems, and other code that needed to use hardware directly. Next, I wanted C++ to be a good language for designing tools. That required flexibility and performance, but also the ability to express elegant interfaces.

And without real changes in user behavior, software suppliers are unlikely to change.

People reward developers who deliver software that is cheap, buggy, and first. That’s because people want fancy new gadgets now.

Software developers have become adept at the difficult art of building reasonably reliable systems out of unreliable parts. The snag is that often we do not know exactly how we did it: a system just “sort of evolved” into something minimally acceptable. Personally, I prefer to know when a system will work, and why it will.

You couldn’t charge people to use Python, for example, any more than you could charge someone to speak English.

What did it mean for a project not to be venture backable? Likely, it met one of the following criteria:There was no business model (ex. no customers to charge, or no advertising model)The project was not structured as a C corpThe project could not conceivably return an investment at venture scale

YCombinator funds nonprofits and now, research. OATV launched indie.vc last year. Peter Thiel created Breakout Labs. Elon Musk created Open AI.

serveStatic: '/static' from: '/var/www/htdocs'

GET: '/divide/<a>/<b>' -> [ :req | (req at: #a) / (req at: #b)];

My hope is that the book I’ve written gives people the courage to realize that this isn’t really about math at all, it’s about power.

n a study cited by the Swiss group last month, researchers found Twitter data alone a more reliable predictor of heart disease than all standard health and socioeconomic measures combined.

Empirically, I trace how civic hackathons in Los Angeles evolved in 2013 - 2015 from engineering exercises to spectacles where civic futures of technology were performed through communication, before turning to particular lessons drawn from civic hackathons. Civic hackathons’ emergence from technical cultures means they often produce conservative civic visions. I pay close attention to moments of failure – moments when possible technologies reproduced existing cultural divisions. Still, I resist describing them as completely co-opted and useless, as I found surprising moments of civic learning and exploration.

This bold claim has led design and critical scholars to hotly debate if participants have a technological ideology imposed on them, or if thinking with technologies enable new civic perspectives.

Winners were praised by sponsors and rewarded with invitations to be part of an accelerator or incubator. Stories from the day generated ample traffic on social media and articles in local newspapers.

Cooperation without coordination can propel democracy to the next level, but it will be met with so much resistance by those currently in power.

EA principles can work in areas outside of global poverty. He was growing the movement the way it ought to be grown, in a way that can attract activists with different core principles rather than alienating them.

Effective altruism is not a replacement for movements through which marginalized peoples seek their own liberationAnd you have to do meta-charity well — and the more EA grows obsessed with AI, the harder it is to do that. The movement has a very real demographic problem, which contributes to very real intellectual blinders of the kind that give rise to the AI obsession. And it's hard to imagine that yoking EA to one of the whitest and most male fields (tech) and academic subjects (computer science) will do much to bring more people from diverse backgrounds into the fold.

But ultimately you have to stop being meta. As Jeff Kaufman — a developer in Cambridge who's famous among effective altruists for, along with his wife Julia Wise, donating half their household's income to effective charities — argued in a talk about why global poverty should be a major focus, if you take meta-charity too far, you get a movement that's really good at expanding itself but not necessarily good at actually helping people.

The other problem is that the AI crowd seems to be assuming that people who might exist in the future should be counted equally to people who definitely exist today. That's by no means an obvious position, and tons of philosophers dispute it. Among other things, it implies what's known as the Repugnant Conclusion: the idea that the world should keep increasing its population until the absolutely maximum number of humans are alive, living lives that are just barely worth living. But if you say that people who only might exist count less than people who really do or really will exist, you avoid that conclusion, and the case for caring only about the far future becomes considerably weaker

The problem is that you could use this logic to defend just about anything. Imagine that a wizard showed up and said, "Humans are about to go extinct unless you give me $10 to cast a magical spell." Even if you only think there's a, say, 0.00000000000000001 percent chance that he's right, you should still, under this reasoning, give him the $10, because the expected value is that you're saving 10^32 lives.

At one point, Russell set about rebutting AI researcher Andrew Ng's comment that worrying about AI risk is like "worrying about overpopulation on Mars," countering, "Imagine if the world's governments and universities and corporations were spending billions on a plan to populate Mars." Musk looked up bashfully, put his hand on his chin, and smirked, as if to ask, "Who says I'm not?"

The second reason for avoiding the GPL is that it does not work well in the context of systems which embed their own code in documents. Programs such as GCC avoid this by having specific exemptions for the embedded code, but it is not possible for us to add this exemption to code taken from elsewhere. This is a problem, since a document in an object-oriented environment is a collection of objects, and objects are a combination of code and data. This could lead to the documents becoming accidentally GPL'd.

But despair is not useful. Despair is paralysis, and there’s work to be done. I feel that this is an essential psychological problem to be solved, and one that I’ve never seen mentioned: How do we create the conditions under which scientists and engineers can do great creative work without succumbing to despair? And how can new people be encouraged to take up the problem, when their instinct is to turn away and work on something that lets them sleep at night?

“It’s more complicated than that.” No kidding. You could nail a list of caveats to any sentence in this essay. But the complexity of these problems is no excuse for inaction. It’s an invitation to read more, learn more, come to understand the situation, figure out what you can build, and go build it. That’s why this essay has 400 hyperlinks. It’s meant as a jumping-off point. Jump off it. There’s one overarching caveat. This essay employed the rhetoric of “problem-solving” throughout. I was trained as an engineer; engineers solve problems. But, at least for the next century, the “problem” of climate change will not be “solved” — it can only be “managed”. This is a long game. One more reason to be thinking about tools, infrastructure, and foundations. The next generation has some hard work ahead of them.

If we must be gods, we should at least be cautious and well-informed gods, with the best possible tools for seeing, understanding, and debating our interventions, and the best possible meta-tools for improving those tools.

A commitment to sourcing every fact is laudible, of course. What’s missing is an understanding of citing and publishing models, not just the data derived from those models. This is a new problem — older media couldn’t generate data on their own. Authors will need to figure out journalistic standards around model citation, and readers will need to become literate in how to “read” a model and understand its limitations.

Imagine an authoring tool designed for arguing from evidence. I don’t mean merely juxtaposing a document and reference material, but literally “autocompleting” sourced facts directly into the document. Perhaps the tool would have built-in connections to fact databases and model repositories, not unlike the built-in spelling dictionary. What if it were as easy to insert facts, data, and models as it is to insert emoji and cat photos?

The importance of models may need to be underscored in this age of “big data” and “data mining”. Data, no matter how big, can only tell you what happened in the past. Unless you’re a historian, you actually care about the future — what will happen, what could happen, what would happen if you did this or that. Exploring these questions will always require models. Let’s get over “big data” — it’s time for “big modeling”.

Readers are thus encouraged to examine and critique the model. If they disagree, they can modify it into a competing model with their own preferred assumptions, and use it to argue for their position. Model-driven material can be used as grounds for an informed debate about assumptions and tradeoffs. Modeling leads naturally from the particular to the general. Instead of seeing an individual proposal as “right or wrong”, “bad or good”, people can see it as one point in a large space of possibilities. By exploring the model, they come to understand the landscape of that space, and are in a position to invent better ideas for all the proposals to come. Model-driven material can serve as a kind of enhanced imagination.

The success of Arduino has had the perhaps retrograde effect of convincing an entire generation that the way to sense and actuate the physical world is through imperative method calls in C++, shuffling bits and writing to ports, instead of in an environment designed around signal processing, control theory, and rapid and visible exploration. As a result, software engineers find a fluid, responsive programming experience on the screen, and a crude and clumsy programming experience in the world.

But the idea that you might implement a control system in an environment designed for designing control systems — it hasn’t been part of the thinking. This leads to long feedback loops, inflexible designs, and guesswork engineering. But most critically, it denies engineers the sort of exploratory environment that fosters novel ideas.

This pejorative reflects a peculiar geocentrism of the programming language community, whose “general-purpose languages” such as Java and Python are in fact very much domain-specific — specific to the domain of software development.

The Gamma: Programming tools for data journalism

I say this despite the fact that my own work has been in much the opposite direction as Julia. Julia inherits the textual interaction of classic Matlab, SciPy and other children of the teletype — source code and command lines.

If efficiency incentives and tools have been effective for utilities, manufacturers, and designers, what about for end users? One concern I’ve always had is that most people have no idea where their energy goes, so any attempt to conserve is like optimizing a program without a profiler.

It’s TCP/IP for energy. Think of these algorithms as hybrids of distributed networking protocols and financial trading algorithms — they are routing energy as well as participating in a market.

The catalyst for such a scale-up will necessarily be political. But even with political will, it can’t happen without technology that’s capable of scaling, and economically viable at scale. As technologists, that’s where we come in.

This is aimed at people in the tech industry, and is more about what you can do with your career than at a hackathon. I’m not going to discuss policy and regulation, although they’re no less important than technological innovation. A good way to think about it, via Saul Griffith, is that it’s the role of technologists to create options for policy-makers.

Any gentrification process inevitably presents two options. Do you abandon the form, leave it to the yuppies and head to the next wild frontier? Or do you attempt to break the cycle, deface the estate-agent signs, and picket outside the wine bar with placards reading ‘Yuppies Go Home’?

I thought was anonymised, but, two or three months later, I was able to de-anonymise it. 

This happened with crowdflow.net, a project about the consolidated.db, the database that records all of your movements on iPhones, that was backed-up unencrypted on PCs and Macs.

Also, the more complex a software project becomes, the more work you have to put into and it grows exponentially. So, keep it simple and make it fast. It's much easier to write software, throw it away and start over again quickly, than having this huge generic system that tries to do everything. It doesn't make sense. It's just too much work. You'd get this huge software system with thousand dependencies and, in the end, it's really hard to innovate, get new stuff in there, or, the worst case, to change the concept. Almost every software that we have published is not generic but is used only for one case. So, keep it simple and get a prototype in under three days.

What type of team do you need to create these visualisations? 
OpenDataCity has a special team of really high-level nerds. Experts on hardware, servers, software development, web design, user experience and so on. I contribute the more mathematical view on the data. But usually a project is done by just one person, who is chief and developer, and the others help him or her. So, it's not like a group project. Usually, it's a single person and a lot of help. That makes it definitely faster, than having a big team and a lot of meetings.

Can you avoid metadata? No, not at all. Sorry, not at all, anymore. I think you can even generate more metadata from existing data. Just think about living near a sightseeing building or something like that, how many tourists took holiday pictures and published them on Facebook. How many of these pictures contains your face? Then, one day, somebody will create a facial recognition algorithm and gather even more metadata about you.

In Pharo, how you meta-click depends on your operating system: either you must hold Shift-Ctrl or Shift-Alt (on Windows or Linux)

Every graphic element of Pharo that you click on...  - With Cmd+Shift+Option,  - you'll get a little menu around the graphic element.

Gemma Hersh, Policy Director at Elsevier explained that “pricing correlates to impact factor." When asked why costs were so high, Hersh stressed that “publishers do not operate a cost based system, [...] we operate a value-based system which reflects the value that we provide."

The essay competition will run until June 15th and will be judged by a committee of scientists, librarians, members of industry, and students based on the following criteria. 

One last piece of advice before beginning your journey: do no try to build any complex visualization in Roassal without first reading the builder infrastructure. Builders may ease your life!

RTLabelled

path := '/Users/alexandrebergel/Documents'. allFilesUnderPath := path asFileReference allChildren. b := RTMondrian new. b shape circle color: Color gray trans; if: [ :aFile | aFile path basename endsWith: '.pdf' ] color: Color red trans. b nodes: allFilesUnderPath. b edges connectFrom: #parent. b normalizer normalizeSize: #size min: 10 max: 150 using: #sqrt. b layout cluster. b

Research stage 1. Case selections and contextualisation of probes by country-leads will be reviewed jointly. Following a state-of-the-art country overview that will gather key insights for the selected case-study and its macro-context, country level workshops will be convened to firm up data-collection plans.

2. broader civil society changes such as emergence of new democratic movements, new citizen-led collaborations.

engagement platform/space, its communications and data architecture (including algorithms) using web-analytics and qualitative methods

A case study methodology has been selected as it is the most appropriate for abstracting broad theoretical insights from specific contextual experiences12. Across eight countries in Asia, Africa, South America and Europe, one case study per country in the area of ICT-mediated citizen engagement will be investigated in depth.

covering dimensions of voice and participation, transparency and accountability, rule of law, equity and inclusion and gender equality to create a tentative index, to be tested by the findings of this study.

The analytical framework will map the shifts along these two categories both from the 'government-end' and 'citizen-end', distilling citizen engagement in emergent meanings, norms and powers. It will also probe tensions – between voice, which may translate as noise on ICT channels, and deliberation that can, without the corresponding right to be heard, end up as discourse dissipation.

Statement of Research Building from empirical specifics of eight case studies from various countries, which will be chosen keeping in mind contextual diversity and institutional maturity, this study will use an analytical framework to address the following questions;RQ1: How do processes of signification, legitimation and domination in ICT-mediated citizen engagement give rise to new governance regimes?RQ2: Under what conditions can ICT mediated citizen engagement support and promote democratic governance?In addition, the study will attempt to develop an index on Transformative Citizen Engagement to evaluate the impact of citizen engagement on democratic governance, testing its efficacy. It will attempt to explain changes to governance systems and develop a layered index (tentatively, Transformative Citizen Engagement Index) that will be tested to evaluate the impact of citizen engagement on democratic governance.

If we do not scrutinise these questions we risk being left with, for example, data without users or analysis without action. Information about tax evasion is toothless without having institutions who are adequately resourced to tackle it. If civil society groups are to stand a chance of effectively counter-balancing corporate influence on political decision-making, they must be equipped with the capacities and legal mechanisms - not just the information - which will enable them to do so.

The paradox, of course, is that Google’s intense data collection and number crunching have led it to the same conclusions that good managers have always known. In the best teams, members listen to one another and show sensitivity to feelings and needs.

However, establishing psychological safety is, by its very nature, somewhat messy and difficult to implement. You can tell people to take turns during a conversation and to listen to one another more. You can instruct employees to be sensitive to how their colleagues feel and to notice when someone seems upset. But the kinds of people who work at Google are often the ones who became software engineers because they wanted to avoid talking about feelings in the first place.

Within psychology, researchers sometimes colloquially refer to traits like ‘‘conversational turn-taking’’ and ‘‘average social sensitivity’’ as aspects of what’s known as psychological safety — a group culture that the Harvard Business School professor Amy Edmondson defines as a ‘‘shared belief held by members of a team that the team is safe for interpersonal risk-taking.’’ Psychological safety is ‘‘a sense of confidence that the team will not embarrass, reject or punish someone for speaking up,’’ Edmondson wrote in a study published in 1999. ‘‘It describes a team climate characterized by interpersonal trust and mutual respect in which people are comfortable being themselves.’’

the good teams all had high ‘‘average social sensitivity’’ — a fancy way of saying they were skilled at intuiting how others felt based on their tone of voice, their expressions and other nonverbal cues. One of the easiest ways to gauge social sensitivity is to show someone photos of people’s eyes and ask him or her to describe what the people are thinking or feeling — an exam known as the Reading the Mind in the Eyes test. People on the more successful teams in Woolley’s experiment scored above average on the Reading the Mind in the Eyes test. They seemed to know when someone was feeling upset or left out. People on the ineffective teams, in contrast, scored below average. They seemed, as a group, to have less sensitivity toward their colleagues.

First, on the good teams, members spoke in roughly the same proportion, a phenomenon the researchers referred to as ‘‘equality in distribution of conversational turn-taking.’’ On some teams, everyone spoke during each task; on others, leadership shifted among teammates from assignment to assignment. But in each case, by the end of the day, everyone had spoken roughly the same amount. ‘‘As long as everyone got a chance to talk, the team did well,’’ Woolley said. ‘‘But if only one person or a small group spoke all the time, the collective intelligence declined.’’

The researchers eventually concluded that what distinguished the ‘‘good’’ teams from the dysfunctional groups was how teammates treated one another. The right norms, in other words, could raise a group’s collective intelligence, whereas the wrong norms could hobble a team, even if, individually, all the members were exceptionally bright.

When pursuing the Early Adopters, don't spend time putting in features that only the Conservatives care about.

So you have to treat them like they are very special.  Give them everything they want, almost as if they were ordering a custom application.  You may have to implement special features just for them.  You may have to give them substantial discounts.  You should visit their site and meet them in person.  You may have to install your product for them. 

But mature languages can't change easily if at all. This situation is described in theory of frozen evolution. Basically the evolution of language is similar of the evolution of a new specie. Evolution happens in short burst when the new language(specie) is created. In this period language design is plastic, there are few if any users, little amount of code and libraries and the community consists of enthusiasts. This short period of plasticity is fallowed by a long period of stability. The evolution of the language is frozen. There are few tweaks here and there but nothing radical. And nobody , not even the original creator could change that.

Clojure has no chance to dislodge Java as premier language for writing enterprise applications, but it could win as the language for writing concurrent applications. That is Clojure platform.

In order to successfully cross the chasm you need a pragmatist in pain.

Lisp isn’t a language, it’s a building material. Alan Kay

In my professional career I’ve noticed that doing “The Right Thing” is not always the best path. That phenomenon is called The Rise of “Worse is Better” and it can be observed in many things including C, Unix, Windows, JavaScript… Seems like New Jersey approach wins out more than it looses in a real world. (Be a virus, spread and than the pressure will rise to better the game).

And then Git happened. Git is so amazingly simple to use that APress, a single publisher, needs to have three different books on how to use it. It’s so simple that Atlassian and GitHub both felt a need to write their own online tutorials to try to clarify the main Git tutorial on the actual Git website. It’s so transparent that developers routinely tell me that the easiest way to learn Git is to start with its file formats and work up to the commands.

And yet, when someone dares to say that Git is harder than other SCMs, they inevitably get yelled at, in what I can only assume is a combination of Stockholm syndrome and groupthink run amok by overdosing on five-hour energy buckets.

You needn’t look further than how many books and websites exist on Git to realize that you are looking at something deeply mucked up.

All of the above might be almost tolerable if DVCSes were easier to use than traditional SCMs, but they aren’t.

The Transmeta architecture assumes from day one that any business plan that calls for making a computer that doesn't run Excel is just not going anywhere.

Conclusion: if you're in a market with a chicken and egg problem, you better have a backwards-compatibility answer that dissolves the problem, or it's going to take you a loooong time to get going (like, forever).

Jon Ross, who wrote the original version of SimCity for Windows 3.x, told me that he accidentally left a bug in SimCity where he read memory that he had just freed. Yep. It worked fine on Windows 3.x, because the memory never went anywhere. Here's the amazing part: On beta versions of Windows 95, SimCity wasn't working in testing. Microsoft tracked down the bug and added specific code to Windows 95 that looks for SimCity. If it finds SimCity running, it runs the memory allocator in a special mode that doesn't free memory right away. That's the kind of obsession with backward compatibility that made people willing to upgrade to Windows 95.

Feature two: old DOS programs assumed they had the run of the chip. As a result, they didn't play well together. But the Intel 80386 had the ability to create "virtual" PCs, each of them acting like a complete 8086, so old PC programs could pretend like they had the computer to themselves, even while other programs were running and, themselves, pretending they had the whole computer to themselves.

That bears mentioning again. WordStar was ported to DOS by changing one single byte in the code.  Let that sink in.

As I have mentioned in previous posts, several platforms have appeared recently that could take on this role of third-party reviewer. I could imagine at least: libreapp.org, peerevaluation.org, pubpeer.com, and publons.com. Pandelis Perakakis mentioned several others as well: http://thomas.arildsen.org/2013/08/01/open-review-of-scientific-literature/comment-page-1/#comment-9.

I think that such third-party review companies should exist for the sole purpose of providing competent, thorough, trust-worthy review of scientific papers and that a focus on profit might divert their attention from this goal. For example, unreasonably high profit margins are one of the reasons that large publishers such as Elsevier are currently being criticised.

Now, pretty much everyone hosts their open source projects on GitHub, including Google, Facebook, Twitter, and even Microsoft—once the bete noire of open source software. In recent months, as Microsoft open sourced some of its most important code, it used GitHub rather than its own open source site, CodePlex. S. “Soma” Somasegar—the 25-year Microsoft veteran who oversees the company’s vast collection of tools for software developers—says CodePlex will continue to operate, as will other repositories like Sourceforge and BitBucket. “We want to make sure it continues being there, as a choice,” he tells WIRED. But he sees GitHub as the only place for a project like Microsoft .NET. “We want to meet developers where they are,” he says. “The open source community, for the most part, is on GitHub.”

Somasegar estimates that about 20 percent of Microsoft’s customers now use Git in some way.

In short, open source has arrived. And, ultimately, that means we can build and shape and improve our world far more quickly than before.

The irony of GitHub’s success, however, is the open source world has returned to a central repository for all its free code. But this time, DiBona—like most other coders—is rather pleased that everything is in one place. Having one central location allows people to collaborate more easily on, well, almost anything. And because of the unique way GitHub is designed, the eggs-in-the-same-basket issue isn’t as pressing as it was with SourceForge. “GitHub matters a lot, but it’s not like you’re stuck there,” DiBona says. While keeping all code in one place, you see, GitHub also keeps it in every place. The paradox shows the beauty of open source software—and why it’s so important to the future of technology.

Instead of, for example, 100 large open source projects with active communities, we’ve got 10,000 tiny repos with redundant functionality.One of open source’s biggest advantages was resilience. A public project with many contributors was theoretically stronger than a private project locked inside a company with fewer contributors.Now, the widespread adoption of open source threatens to create just the opposite.

What makes this more difficult to resolve is that GitHub is — surprise! — not open source. GitHub is closed source, meaning that only GitHub staff is able to make improvements to its platform.The irony of using a proprietary tool to manage open source projects, much like BitKeeper and Linux, has not been lost on everyone. Some developers refuse to put their code on GitHub to retain their independence. Linus Torvalds, the creator of Git himself, refuses to accept pull requests (code changes) from GitHub.

But it comes with new challenges: how to actually manage demand and workflows, how to encourage contributions, and how to build antifragile ecosystems.

Technically, if you use someone else’s code revision from Stack Overflow, you would have to add a comment in your code that attributes the code to them. And then that person’s code would potentially have a different license from the rest of your code.Your average hobbyist developer might not care about the rules, but many companies forbid employees from using Stack Overflow, partly for this reason.As we enter a post open source world, Stack Overflow has explored transitioning to a more permissive MIT license, but the conversation hasn’t been easy. Questions like what happens to legacy code, and dual licensing for code and non-code contributions, have generated confusion and strong reactions.

As a result, while plenty of amateur developers use open source projects, those people aren’t interested in, or capable of, seriously giving back. They might be able to contribute a minor bug or fix, but the heavy lifting is still left to the veterans.

Experienced maintainers have felt the burden. Today, open source looks less like a two-way street, and more like free products that nobody pays for, but that still require serious hours to maintain.This is not so different from what happened to newspapers or music, except that nearly all the world’s software is riding on open source.

There is also concern around using a centralized platform to manage millions of repositories: GitHub has faced several outages in recent years, including a DDoS attack last year and a network disruption just yesterday. A disruption in just one website — GitHub — affects many more.Earlier this month, a group of developers wrote an open letter to GitHub, expressing their frustration with the lack of tools to manage an ever-increasing work load, and requesting that GitHub make important changes to its product.

The free software generation had to think about licenses because they were taking a stance on what they were not (that is, proprietary software). The GitHub generation takes this right for granted. They don’t care about permissions. They default to open.Open source is so popular today that we don’t think of it as exceptional anymore. We’re so open source, that maybe we’re post open source:But not is all groovy in the land of post open source.

n 2011, there were 2 million repositories on GitHub. Today, there are over 29 million. GitHub’s Brian Doll noted that the first million repositories took nearly 4 years to create; getting from nine to ten million took just 48 days.

Now developers had all the tools they needed. In the 1980s, they had to use a scattered combination of IRC, mailing lists, forums, and version control systems.By 2010, they had Git for version control, GitHub to collaborate, and Stack Overflow to ask and answer questions.

A quote often attributed to Gloria Steinem says: “We’ve begun to raise daughters more like sons... but few have the courage to raise our sons more like our daughters.” Maker culture, with its goal to get everyone access to the traditionally male domain of making, has focused on the first. But its success means that it further devalues the traditionally female domain of caregiving, by continuing to enforce the idea that only making things is valuable. Rather, I want to see us recognize the work of the educators, those that analyze and characterize and critique, everyone who fixes things, all the other people who do valuable work with and for others—above all, the caregivers—whose work isn’t about something you can put in a box and sell.

am not a maker. In a framing and value system is about creating artifacts, specifically ones you can sell, I am a less valuable human. As an educator, the work I do is superficially the same, year on year. That’s because all of the actual change, the actual effects, are at the interface between me as an educator, my students, and the learning experiences I design for them. People have happily informed me that I am a maker because I use phrases like "design learning experiences," which is mistaking what I do (teaching) for what I’m actually trying to help elicit (learning). To characterize what I do as "making" is to mistake the methods—courses, workshops, editorials—for the effects. Or, worse, if you say that I "make" other people, you are diminishing their agency and role in sense-making, as if their learning is something I do to them.

In Silicon Valley, this divide is often explicit: As Kate Losse has noted, coders get high salary, prestige, and stock options. The people who do community management—on which the success of many tech companies is based—get none of those. It’s unsurprising that coding has been folded into "making." Consider the instant gratification of seeing "hello, world" on the screen; it’s nearly the easiest possible way to "make" things, and certainly one where failure has a very low cost. Code is "making" because we've figured out how to package it up into discrete units and sell it, and because it is widely perceived to be done by men.

It’s not, of course, that there’s anything wrong with making (although it’s not all that clear that the world needs more stuff).

Almost all the artifacts that we value as a society were made by or at the order of men. But behind every one is an invisible infrastructure of labor—primarily caregiving, in its various aspects—that is mostly performed by women.

Below I list a few advantages and drawbacks of anonymity where I assume that a drawback of anonymous review is an advantage of identified review and vice versa. Drawbacks Reviewers do not get credit for their work. They cannot, for example, reference particular reviews in their CVs as they can with publications. It is relatively “easy” for a reviewer to provide unnecessarily blunt or harsh critique. It is difficult to guess if the reviewer has any conflict of interest with the authors by being, for example, a competing researcher interested in stalling the paper’s publication. Advantages Reviewers do not have to fear “payback” for an unfavourable review that is perceived as unfair by the authors of the work. Some (perhaps especially “high-profile” senior faculty members) reviewers might find it difficult to find the time to provide as thorough a review as they would ideally like to, yet would still like to contribute and can perhaps provide valuable experienced insight. They can do so without putting their reputation on the line.

With most journals, if I submit a paper that is rejected, that information is private and I can re-submit elsewhere. In open review, with a negative review one can publicly lose face as well as lose the possibility of re-submitting the paper. Won’t this be a significant disincentive to submit? This is precisely what we are trying to change. Currently, scientists can submit a paper numerous times, receive numerous negative reviews and ultimately publish their paper somewhere else after having “passed” peer review. If scientists prefer this system then science is in a dangerous place. By choosing this model, we as scientists are basically saying we prefer nice neat stories that no one will criticize. This is silly though because science, more often than not, is not neat and perfect. The Winnower believes that transparency in publishing is of the utmost importance. Going from a closed anonymous system to an open system will be hard for many scientists but I believe that it is the right thing to do if we care about the truth.

At what point does payment occur, and are you concerned with the possible perception that this is pay-to-publish? Payment occurs as soon as you post your paper online. I am not overly concerned with the perception that this is pay-to-publish because it is. What makes The Winnower different is the price we charge. Our price is much much lower than what other journals charge and we are clear as to what its use will be: the sustainability and growth of the website. arXiv, a site we are very much modeled after does not charge anything for their preprint service but I would argue their sustainability based on grants is questionable. We believe that authors should buy into this system and we think that the price we will charge is more than fair. Ultimately, if a critical mass is reached in The Winnower and other revenue sources can be generated than we would love to make publishing free but at this moment it is not possible.

I strongly believe that if you’re scared of open peer review then we should be scared of your results.

While The Winnower won’t eliminate bias (we are humans, after all) the content of the reviews can be evaluated by all because they will be readily accessible. [Note: reviewers could list competing interests in the template suggested on The Winnower’s blog.]

Moreover, editors are literally selecting for simple studies but very often studies are not simple and results are not 100% clear. If you can’t publish your work because it is honest but poses some questions then eventually you will have to mold your work to what an editor wants and not what the data is telling you. There is a significant correlation between impact factor and misconduct and it is my opinion that much of this stems from researchers bending the truth, even if ever so slightly, to get into these career advancing publications.

PLOS Labs is working on establishing structured reviews and we have talked with them about this.

It should be noted that papers will always be open for review so that a paper can accumulate reviews throughout its lifetime.

The journal will accommodate data but should be presented in the context of a paper. The Winnower should not act as a forum for publishing data sets alone. It is our feeling that data in absence of theory is hard to interpret and thus may cause undue noise to the site.

I think The Winnower has found a nice niche publishing what is called “grey literature.” (i.e. we publish content that is not traditionally afforded a platform).  By focusing on this niche in the in the short term (<5years) we can build a community that will allows us to experiment with different models in the long term (>5Years).  I found out very early after launch of The Winnower—it’s not enough to build a platform around a new model, you have to convey the value to the community and really incentivize people to use it.

I am hoping to change scholarly communication at all levels and I think transparency must be at the heart of this.

While there are some features shared between a university repository and us we are distinctly different for the following reasons: We offer DOIs to all content published on The Winnower All content is automatically typeset on The Winnower Content published on the winnower is not restricted to one university but is published amongst work from peers at different institutions around the world Work is published from around the world it is more discoverable We offer Altmetrics to content  Our site is much more visually appealing than a typical repository  Work can be openly reviewed on The Winnower but often times not even commented on in repositories. This is not to say that repositories have no place, but that we should focus on offering authors choices not restricting them to products developed in house.

20 years: ideas and results will be communicated iteratively and dynamically, not as a story written in stone. There is an increasing number of artifacts beyond text (data, visualizations, software tools, code, spreadsheets, multimedia content, etc.) How might these outputs factor into the scholarly conversation and more directly, the tenure and promotion process? JN: I think all these various outputs you mention are gaining prominence in scholarly communication.  I think that will continue and will become more and more important in how scholars are evaluated and rightly so a lot of work is done in different mediums and outside the confines of the article.  We need to experiment with different approaches of evaluation and part of that is looking beyond one thing (how often you publish and where you publish).  We do need to be careful though as new systems are implemented, new is not necessarily better.

I think the next generation of scientists who have grown up in the Internet era will have zero patience for the current system and because of that they will seek different outlets that make sense in light of the fact the Internet exists!  

Most importantly, is that we’ve given the tools of scholarly publishers to the scholars themselves to use.  Which has had the unexpected effect that different types of content are being produced (conference proceedings, grants, open letters, responses to grants, peer reviews, logistics for organizing symposiums, and more).  Ultimately, we’ve created a platform that allows anyone to get their idea out there and to be afforded the same tools that a traditional publisher offers, that is in my opinion quite impactful.

developing countries and developing scientists (students) are left out of scholarly discourse,

Esta licencia de PI finaliza cuando eliminas tu contenido de PI o tu cuenta, salvo si el contenido se compartió con terceros y estos no lo eliminaron.

En el caso de contenido protegido por derechos de propiedad intelectual, como fotos y videos ("contenido de PI"), nos concedes específicamente el siguiente permiso, de acuerdo con la configuración de la privacidad y de las aplicaciones: nos concedes una licencia no exclusiva, transferible, con derechos de sublicencia, libre de regalías y aplicable en todo el mundo para utilizar cualquier contenido de PI que publiques en Facebook o en conexión con Facebook ("licencia de PI").

Fecha de la última revisión: 30 de enero de 2015

Green OA and the role of repositories remain controversial. This is perhaps less the case for institutional repositories, than for subject repositories, especially PubMed Central. The lack of its own independent sustainable business model means Green OA depends on its not undermining that of (subscription) journals. The evidence remains mixed: the PEER project found that availability of articles on the PEER open repository did not negatively impact downloads from the publishers’s site, but this was contrary to the experience of publishers with more substantial fractions of their journals’ content available on the longer-established and better-known arXiv and PubMed Central repositories. The PEER usage data study also provided further confirmation of the long usage half-life of journal articles and its substantial variation between fields (suggesting the importance of longer embargo periods than 6–12 months, especially for those fields with longer usage half-lives). Green proponents for their part point to the continuing profitability of STM publishing, the lack of closures of existing journals and the absence of a decline in the rate of launch of new journals since repositories came online as evidence of a lack of impact to date, and hence as evidence of low risk of impact going forward. Many publishers’ business instincts tell them otherwise; they have little choice about needing to accept submissions from large funders such as NIH, but there has been some tightening of publishers’ Green policies (page 102).

Research funders are playing an increasingly important role in scholarly communication. Their desire to measure and to improve the returns on their investments emphasises accountability and dissemination. These factors have been behind their support of and mandates for open access (and the related, though less contentious policies on data sharing). These policies have also increased the importance of (and some say the abuse of) metrics such as Impact Factor and more recently are creating part of the market for research assessment services (page88).

Open access publishing has led to the emergence of a new type of journal, the so-called megajournal. Exemplified by PLOS ONE, the megajournal is characterised by three features: full open access with a relatively low publication charge; rapid “non-selective” peer review based on “soundness not significance” (i.e. selecting papers on the basis that science is soundly conducted rather than more subjective criteria of impact, significance or relevance to a particularly community); and a very broad subject scope. The number of megajournals continues to grow: Table 10 lists about fifty examples (page 99).

and the more research intensive universities remain concerned about the net impact on their budgets (page 90; 123).

Gold open access based on APCs has a number of potential advantages. It would scale with the growth in research outputs, there are potential system-wide savings, and reuse is simplified. Research funders generally reimburse publication charges, but even with broad funder support the details regarding the funding arrangements within universities it remain to be fully worked out. It is unclear where the market will set OA publication charges: they are currently lower than the historical average cost of article publication; about 25% of authors are from developing countries;

The APC model itself has become more complicated, with variable APCs (e.g. based on length), discounts, prepayments and institutional membership schemes, offsetting and bundling arrangements for hybrid publications, an individual membership scheme, and so on (page 91; 93).

Average publishing costs per article vary substantially depending on a range of factors including rejection rate (which drives peer review costs), range and type of content, levels of editorial services, and others. The average 2010 cost of publishing an article in a subscription-based journal with print and electronic editions was estimated by CEPA to be around £3095 (excluding non-cash peer review costs). The potential for open access to effect cost savings has been much discussed, but the emergence of pure-play open access journal publishers allows examples of average article costs to be inferred from their financial statements. These range from $290 (Hindawi), through $1088 (PLOS), up to a significantly higher figure for eLife (page 66).

There is continued interest in expanding access by identifying and addressing these specific barriers to access or access gaps. While open access has received most attention, other ideas explored have included increased funding for national licences to extend and rationalise cover; walk-in access via public libraries (a national scheme was piloted in the UK in 2014); the development of licences for sectors such as central and local government, the voluntary sector, and businesses (page 84)

The most commonly cited barriers to access are cost barriers and pricing, but other barriers cited in surveys include: lack of awareness of available resources; a burdensome purchasing procedure; VAT on digital publications; format and IT problems; lack of library membership; and conflict between the author’s or publisher’s rights and the desired use of the content (page 84).

While publishers have always provided services such as peer review and copy-editing, increased competition for authors, globalisation of research, and new enabling technologies are driving an expansion of author services and greater focus on improving the author experience. One possibly emerging area is that of online collaborative writing tools: a number of start-ups have developed services and some large publishers are reported to be exploring this area (page 153).

Semantic technologies have become mainstream within STM journals, at least for the larger publishers and platform vendors. Semantic enrichment of content (typically using software tools for automatic extraction of metadata and identification and linking of entities) is now widely used to improve search and discovery; to enhance the user experience; to enable new products and services; and for internal productivity improvements. The full-blown semantic web remains some way off, but publishers are starting to make use of linked data, a semantic web standard for making content more discoverable and re-usable (page 143).

The growing importance to funders and institutions of research assessment and metrics has been reflected in the growth of information services such as research analytics built around the analysis of metadata (usage, citations, etc.), and the growth of a new software services such as CRIS tools (Current Research Information Systems) (page 150).

Text and data mining are starting to emerge from niche use in the life sciences industry, with the potential to transform the way scientists use the literature. It is expected to grow in importance, driven by greater availability of digital corpuses, increasing computer capabilities and easier-to-use software, and wider access to content

The explosion of data-intensive research is challenging publishers to create new solutions to link publications to research data (and vice versa), to facilitate data mining and to manage the dataset as a potential unit of publication. Change continues to be rapid, with new leadership and coordination from the Research Data Alliance (launched 2013): most research funders have introduced or tightened policies requiring deposit and sharing of data; data repositories have grown in number and type (including repositories for “orphan” data); and DataCite was launched to help make research data cited, visible and accessible. Meanwhile publishers have responded by working closely with many of the community-led projects; by developing data deposit and sharing policies for journals, and introducing data citation policies; by linking or incorporating data; by launching some pioneering data journals and services; by the development of data discovery services such as Thomson Reuters’ Data Citation Index (page 138).

Similarly the rapid general adoption of mobile devices (smartphones and tablets) has yet to change significantly the way most researchers interact with most journal content–accesses from mobile devices still account for less than 10% of most STM platform’s traffic as of 2014 (though significantly higher in some fields such as clinical medicine) –but this is changing. Uptake for professional purposes has been fastest among physicians and other healthcare professionals, typically to access synoptic secondary services, reference works or educational materials rather than primary research journals. For the majority of researchers, though, it seems that “real work” still gets done at the laptop or PC (page 24; 30; 139).

Social networks and other social media have yet to make the impact on scholarly communication that they have done on the wider consumer web. The main barriers to greater use have been the lack of clearly compelling benefits to outweigh the real costs (e.g. in time) of adoption. Quality and trust issues are also relevant: researchers remain cautious about using means of scholarly communication not subject to peer review and lacking recognised means of attribution. Despite these challenges, social media do seem likely to become more important given the rapid growth in membership of the newer scientific social networks (Academia, Mendeley, ResearchGate), trends in general population, and the integration of social features into publishing platforms and other software (page 72; 134).

Virtually all STM journals are now available online, and in many cases publishers and others have retrospectively digitised early hard copy material back to the first volumes. The proportion of electronic-only journal subscriptions has risen sharply, partly driven by adoption of discounted journal bundles. Consequently the vast majority of journal use takes place electronically, at least for research journals, with print editions providing some parallel access for some general journals, including society membership journals, and in some fields (e.g. humanities and some practitioner fields). The number of established research (i.e. non-practitioner) journals dropping their print editions looks likely to accelerate over the coming few years (page 30).

There is a significant amount of innovation in peer review, with the more evolutionary approaches gaining more support than the more radical. For example, some variants of open peer review (e.g. disclosure of reviewer names either before or after publication; publication of reviewer reports alongside the article) are becoming more common. Cascade review (transferring articles between journals with reviewer reports) and even journal-independent (“portable”) peer review are establishing a small foothold. The most notable change in peer review practice, however, has been the spread of the “soundness not significance” peer review criterion adopted by open access “megajournals” like PLOS ONE and its imitators. Post-publication review has little support as a replacement for conventional peer review but there is some interest in its use as a complement to it (for example, the launch of PubMed Commons is notable in lending the credibility of PubMed to post-publication review). There is similar interest in “altmetrics” as a potentially useful complement to review and in other measures of impact. A new technology of potential interest for post-publication review is open annotation, which uses a new web standard to allow citable comments to be layered over any website (page 47).

Reading patterns are changing, however, with researchers reading more, averaging 270 articles per year, depending on discipline (more in medicine and science, fewer in humanities and social sciences), but spending less time per article, with reported reading times down from 45-50 minutes in the mid-1990s to just over 30 minutes. Access and navigation to articles is increasingly driven by search rather than browsing; at present there is little evidence that social referrals are a major source of access (unlike consumer news sites, for example), though new scientific social networks may change this. Researchers spend very little time on average on publisher web sites, “bouncing” in and out and collecting what they need for later reference (page 52).

Despite a transformation in the way journals are published, researchers’ core motivations for publishing appear largely unchanged, focused on securing funding and furthering the author’s career (page 69)

Although this report focuses primarily on journals, the STM book market (worth about $5 billion annually) is evolving rapidly in a transition to digital publishing. Ebooks made up about 17% of the market in 2012 but are growing much faster than STM books and than the STM market as a whole (page 24).

The annual revenues generated from English-language STM journal publishing are estimated at about $10 billion in 2013, (up from $8 billion in 2008, representing a CAGR of about 4.5%), within a broader STM information publishing market worth some $25.2 billion. About 55% of global STM revenues (including non-journal STM products) come from the USA, 28% from Europe/Middle East, 14% from Asia/Pacific and 4% from the rest of the world (page 23).

There's no incentive structure for people to comment extensively, because it can take time to write a thoughtful comment, and one currently doesn't get credit for it,” he says. “But it's an experiment that needs to be done.”

At the moment, Neylon explains, the scholarly publishing process involves ferrying a document from place to place. Researchers prepare manuscripts, share them with colleagues, fold in comments and submit them to journals. Journal editors send copies to peer reviewers, returning their comments to the author, who goes back and forth with the editor to finalize the text. After publication, readers weigh in with commentary of their own.

To jump-start interest in the annotation program, arXiv has been converting mentions of its articles in external blog posts (called trackbacks) into annotations that are visible on an article's abstract page when using Hypothes.is.

The scientific publisher eLife in Cambridge, UK, has been testing the feasibility of using Hypothes.is to replace its peer-review commenting system, says Ian Mulvany, who heads technology at the firm. The publisher plans to incorporate the annotation platform in a site redesign instead of its current commenting system, Disqus. At a minimum, says Mulvany, Hypothes.is provides a mechanism for more-targeted commentary — the equivalent of moving comments up from the bottom of a web page into the main body of the article itself.

The digital library JSTOR, for example, is developing a custom Hypothes.is tool for its educational project with the Poetry Foundation, a literary organization and publisher in Chicago, Illinois.

That should enable the tool to be used for journal clubs, classroom exercises and even peer review.

But unlike Hypothes.is, the Genius code is not open-source, its service doesn't work on PDFs, and it is not working with the scholarly community.

A few websites today have inserted code that allows annotations to be made on their pages by default, including the blog platform Medium, the scholarly reference-management system F1000 Workspace and the news site Quartz. However, annotations are visible only to users on those sites. Other annotation services, such as A.nnotate or Google Docs, require users to upload documents to cloud-computing servers to make shared annotations and comments on them.

It's always a strange thing, going from nothing to something. Starting with just an idea, and gradually turning it into something real. Inventing along the way all these things that start so small, and gradually become these whole structures. I always think anyone who's been involved in such a thing has kind of a glow of confidence that lasts at least a decade—realizing that, yes, with the right effort nothing can turn into something.

The difference is that with the Smalltalk code I can let the message do the talking. The message initiates the action. With C# I have to call a method in order to "send a message." This is what OO is supposed to avoid, because we're exposing implementation, here. It reifies the abstraction.

A fundamental difference between the way Smalltalk treats objects and the way other so-called OOP languages treat them is that objects, as Alan Kay envisioned them (he coined the term "object-oriented"), are really meant to be servers (in software, not necessarily hardware), not mere collections of functions that have privileged access to abstract data types.

The fundamental principle of objects in Smalltalk is message passing. What matters is what goes on between objects, not the objects themselves. The abstraction is in the message passing, not the objects.

The idea was to create a "no-centers" system design, where logic, and operational control is distributed, not centralized.

v := RTView new. s := (RTBox new size: 30) + RTLabel. es := s elementsOn: (1 to: 20). v addAll: es. RTGridLayout on: es. v

| visual composedShape data viewElements |
visual := RTView new.
data := #('lion-o' 'panthro' 'tigro' 'chitara' 'munra' 'ozimandias' 'Dr Manhatan').
composedShape := (RTEllipse new size: 100; color: Color veryLightGray) + RTLabel.
viewElements := composedShape elementsOn: data.
visual addAll: viewElements. 
RTGridLayout on: viewElements.
visual

Roassal maps objects and connections to graphical elements and edges. In additions, values and metrics are represented in visual dimentions (e.g., width, height, intensity of graphical elements). Such mapping is an expressive way to build flexible and rich visualization. This chapter gives an overview of Roassal and its main API. It covers the essential concepts of Roassal, such as the view, elements, shapes, and interactions.

Once a Roassal element has been created, modifying its shape should not result in an update of the element.

c := TRCanvas new. shape := TRBoxShape new size: 40. c addShape: shape. shape when: TRMouseClick do: [ :event | event shape remove. c signalUpdate ]. c

| canvas myCircle data |
canvas := TRCanvas new.
myCircle := TREllipseShape new size: 100; color: Color white.
data := #('lion-o' 'panthro' 'tigro' 'chitara' 'munra' 'ozimandias' 'Dr Manhatan').
canvas addShape: myCircle.
myCircle when: TRMouseClick do: [:event | event myCircle remove. canvas signalUpdate  ].
canvas

Any sophisticated visualization boils down into primitives graphical elements. Ultimately, no matter how complex a visualization is, circles, boxes, labels are the bricks to convey information. Before jumping into Roassal, it is important to get a basic understand on these primitive graphical elements. Trachel is a low-level API to draw primitive graphical elements.

Our utopian visions of the future, freed from present problems by human ingenuity and technical competence, might be possible on paper, but they are unlikely in reality. We have already made the biggest mistake, and spent 10,000 years perfecting a disastrous invention, then making ourselves ever more reliant on it. However, the archaeologists who give us glimpses of our ancestors, and the anthropologists who introduce us to our cousins, have been able to show us why we dream what we do. What we yearn for is not just our imagined future; it is our very real past.