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  1. Last 7 days
    1. Success ina data science project comes not from access to any one exotic tool, but from having quantifiablegoals, good methodology, crossdiscipline interactions, and a repeatable workflow.

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    1. So many people today – and even professional scientists – seem to me like somebody who has seen thousands of trees but has never seen a forest. A knowledge of the historic and philosophical background gives that kind of independence from prejudices of his generation from which most scientists are suffering.

      a nice way to put it

  2. Jun 2019
  3. May 2019
    1. Scientists can find the latest data and analysis on their areas of research, determine experiments that have already been performed that they don’t need to replicate and find new opportunities for investigation

      "Don't need to replicate"!!! A big part of science is the ability to exactly replicate and double check others' work! We need the ability to do more replication, not less!

    1. The first difficulty is that the robot’s utility function did not quite match our utility function. Our utility function is 1 if the cauldron is full, 0 if the cauldron is empty, −10 points to whatever the outcome was if the workshop has flooded, +0.2 points if it’s funny, −1,000 points (probably a bit more than that on this scale) if someone gets killed … and it just goes on and on and on.

      But it is very difficult to fully express these utility functions in code. The goal is to literally turn our ethics into code -- to translate them into coherent data structures, algorithms, and decision trees. We want to deduce our moral intuitions and more.

    1. People are rewarded for being productive rather than being right, for building ever upward instead of checking the foundations. These incentives allow weak studies to be published. And once enough have amassed, they create a collective perception of strength that can be hard to pierce.

      We desperately need to fix these foundations of science to focus on solid foundations and reproducibility...

    1. High-level bodies such as the US National Academies of Sciences, Engineering, and Medicine and the European Commission have called for science to become more open and endorsed a set of data-management standards known as the FAIR (findable, accessible, interoperable and reusable) principles.
  4. Apr 2019
    1. Women in science are cited less than their male colleagues. They have a harder time getting work published in notable journals, including the flagships Science and Nature. They are likely paid less than their peers (a 2013 study found that women working in physics and astronomy were paid 40 percent less than men). And they are more likely to face workplace harassment.
    2. Researchers are protesting grant processes that overwhelmingly fund male-led projects, and scientific societies are reforming their sexual harassment policies.
    1. A Vision for Scholarly Communication Currently, there is a strong push to address the apparent deficits of the scholarly communication system. Open Science has the potential to change the production and dissemination of scholarly knowledge for the better, but there is no commonly shared vision that describes the system that we want to create.

      A Vision for Scholarly Communication

  5. Mar 2019
    1. Coral reefs are projected to decline by a further 70-90% at 1.5°C.

      How will that effect the species that rely on the reefs for shelter? Will some be able to survive?

    1. The main purpose of the Discovery IN is to provide interfaces and other user-facing services for data discovery across disciplines. We explore new and innovative ways of enabling discovery, including visualizations, recommender systems, semantics, content mining, annotation, and responsible metrics. We apply user involvement and participatory design to increase usability and usefulness of the solutions. We go beyond academia, involving users from all stakeholders of research data. We create FAIR and open infrastructures, following the FAIR principles complemented by the principles of open source, open data, and open content, thus enabling reuse of interfaces and user-facing services and continued innovation. Our main objectives are:
    1. To investigate whether and how user data are shared by top rated medicines related mobile applications (apps) and to characterise privacy risks to app users, both clinicians and consumers.

      "24 of 821 apps identified by an app store crawling program. Included apps pertained to medicines information, dispensing, administration, prescribing, or use, and were interactive."

    1. This page enables one to download the book "How People Learn" for free and allows one to link to related content. This book was not originally written for adult learning but is included here because it is a valuable resource, an entire book provided for free, with immediate relevance to adult learning even if every example, etc. is not based on adult learning. Rating 4/5

    1. This link is for the Association of Information Science and Technology. While many of the resources are available only to those who are association members, there are a great many resources to be found via this site. Among the items available are their newsletter and their journal articles. As the title suggests, there is a technology focus, and also a focus on scientific findings that can guide instructional designers in the presentation and display of visual and textual information, often but not exclusively online. Instructional designers are specifically addressed via the content of this site. A student membership is available. Rating 5/5

    1. Open science is the movement to make scientific research, data and dissemination accessible to all levels of an inquiring society.

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    1. Open Access publishing and Open Science MENU

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    1. Center for Open Science

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    1. Open Science MOOC

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    1. Open Science Directory

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    1. October 21 - 27, 2019

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    1. How open science helps researchers succeed

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      DOI:10.7554/eLife.16800 PMCID: PMC4973366 PMID: 27387362 OA

    1. Open Science Grid About News Contact

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    1. Open Science Prize

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    1. Open Science Manifesto

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    1. Festival de la ciencia abierta y participativa

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    1. Open Science Days

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    1. Open Research Facilitating faster and more effective research discovery

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    1. A nano-porous carbon composite membrane has been found to display high water flux due to exceptionally high surface diffusion, together with an excellent salt rejection [2616, 2958].

      With an excellent ability to reject salt, how often does membrane fouling become an issue when desalinating seawater? to a point where it causes water flux decline and lowers the quality of the water produced.

      ~ Anthony Y.

  6. Feb 2019
    1. As Shulman (1986) noted, this knowledge would include knowledge of concepts, theories, ideas, organizational frameworks, knowledge of evidence and proof, as well as established practices and approaches toward developing such knowledge. Knowledge and the nature of inquiry differ greatly between fields, and teachers should understand the deeper knowledge fundamentals of the disciplines in which they teach

      It is important to not only understand what the content is that we are teaching but to understand what goes into the content that we are teaching. The article gives exampled of art and science; the importance is not only on the art or science it is the history and understanding of artists and their meaning and "knowledge of scientific facts and theories, the scientific method, and evidence-based reasoning"

    1. Research methodologies and methods used must be open for full discussion and review by peers and stakeholders.

      So does this mean totally open? As in publish your protocols open?

    1. we don't know how to run the experiments that would falsify the hypotheses — the energies are too high, the accelerators too expensive, and so on.

      We essentially cannot collect enough data and have to resort to speculation. Is this true?

    1. creating obscurities through disputation,

      lol @ this.

      "creating obscurities through disputation" sounds an awful lot like "broadening the knowledge base of humanity." Arguing toward ever more precise ideas and their articulations is the driving force of the Enlightenment.

    1. every individual has the means to decide how their knowledge is governed and managed to address their needs
    2. knowledge commons

      The idea of a "knowledge commons" was referenced in the book, "Campesino a Campesino: Voices from Latin America’s Farmer to Farmer Movement for Sustainable Agriculture" by Eric Holt-Giménez in the context of agroecological knowledge inherent in agrarian communities in Latin America.

  7. Jan 2019
    1. abstract independently existing “object”

      Since forever, apparently, science has relied on Aristotle's "Unmoved Mover," in a sense. Not a god exactly, but some real or imagined unaffected observer whose presence serves as a fixed point from which to accumulate data. Why are we tempted to think this way? Aren't we all moving? What fixed point is there? I'm tempted to go back to the analogy of floating baskets tied together. There is an illusion of being grounded, but we aren't really.

    1. This is the meaning of the “Day of Resurrection,” spoken of in all the scriptures, and announced unto all people. Reflect, can a more precious, a mightier, and more glorious day than this be conceived, so that man should willingly forego its grace, and deprive himself of its bounties, which like unto vernal showers are raining from the heaven of mercy upon all mankind?

      I think this meaning is that "Resurrection" is the return of a Manifestation in another human frame. And this is stated to be clearly more glorious than the literal interpretations of past scripture.

      Why is it clearly more glorious?

      1. Everyone has access. And it leads to empowerment.
      2. It allows us to keep science, which is pretty awesome.
      3. It doesn't allow us to just wait for the rapture - see point 1 about empowerment.
      4. It allows us to see all religions as united in spirit.
      5. Related to point 3 and 4, it allows us to unite with non-religious people.
      6. All of this without "doing violence to the facts".
    1. This is one of the most important decisions in an EFA (Thompson, 2004; Warne & Larsen, 2014), and these decisions can make g artificially easier or harder to identify.

      Deciding the number of factors to retain can be extremely subjective. But that was why it was so important to pre-register our work. We wanted to choose methods for making this decision before seeing the data so that no one could accuse of us of trying to monkey with the data until we got the results we wanted.

    2. For the sake of transparency, we find it important to explicitly state deviations from our preregistration protocol. First, in our preregistration, we stated that we would search for (cognitive OR intelligence) AND the name of a continent or population. However, searching for a continent was not feasible in finding data sets. We also had difficulty generating a list of population groups (e.g., ethnic groups, tribal groups) that would be useful for our search procedures.

      This was my second time I pre-registered the study and the first time my student co-author had. We are still getting the hang of it.

    1. he would extend this to "science" tout court-does not use value-free lan-guage, that value-free language does not exist, and that we cannot posit a purely transparent language devoid of distracting ornament, through which we transact business with pure facts.

      This reminds me of an article I read in my Feminist Epistemologies class, "The Egg and the Sperm: How Science Has Constructed a Romance Based on Stereotypical Male-Female Roles," which shook me to my core. It argues that science and culture are intertwined and that they influence and reinforce one another. The scientific descriptions of egg, sperm, reproduction, and ovulation she provides to support her argument show how dangerous the perpetuation of the idea of "value-free" and/or unbiased language can be (and is).

    2. Value-free language and the possibility of a self-contained discipline make possible both modern sci-ence and that mapping of humanistic inquiry onto a scientific model which has created modern social science as well.

      And yet, any mapping of humanistic inquiry onto a scientific model would lead to the creation of incomplete maps, of certain lies. One of those lies? If you can't use the scientific method to come to know something, then that something isn't knowledge/true/truth/fact.

    1. A comment at the bottom by Barbara H Partee, another panelist alongside Chomsky:

      I'd like to see inclusion of a version of the interpretation problem that reflects my own work as a working formal semanticist and is not inherently more probabilistic than the formal 'generation task' (which, by the way, has very little in common with the real-world sentence production task, a task that is probably just as probabilistic as the real-world interpretation task).

    2. There is a notion of success ... which I think is novel in the history of science. It interprets success as approximating unanalyzed data.

      This article makes a solid argument for why statistical and probabilistic models are useful, not only for prediction, but also for understanding. Perhaps this is a key point that Noam misses, but the quote narrows the definition to models that approximate "unanalyzed data".

      However, it seems clear from this article that the successes of ML models have gone beyond approximating unanalyzed data.

    3. But O'Reilly realizes that it doesn't matter what his detractors think of his astronomical ignorance, because his supporters think he has gotten exactly to the key issue: why? He doesn't care how the tides work, tell him why they work. Why is the moon at the right distance to provide a gentle tide, and exert a stabilizing effect on earth's axis of rotation, thus protecting life here? Why does gravity work the way it does? Why does anything at all exist rather than not exist? O'Reilly is correct that these questions can only be addressed by mythmaking, religion or philosophy, not by science.

      Scientific insight isn't the same as metaphysical questions, in spite of having the same question word. Asking, "Why do epidemics have a peak?" is not the same as asking "Why does life exist?". Actually, that second question can be interested in two different ways, one metaphysically and one physically. The latter interpretation means that "why" is looking for a material cause. So even simple and approximate models can have generalizing value, such as the Schelling Segregation model. There is difference between models to predict and models to explain, and both have value. As later mentioned in this document, theory and data are two feet and both are needed for each other.

    4. This page discusses different types of models

      • statistical models
      • probabilistic models
      • trained models

      and explores the interaction between prediction and insight.

    5. Chomsky (1991) shows that he is happy with a Mystical answer, although he shifts vocabulary from "soul" to "biological endowment."

      Wasn't one of Chomsky's ideas that humans are uniquely suited to language? The counter-perspective espoused here appears to be that language emerges, and that humans are only distinguished by the magnitude of their capacity for language; other species probably have proto-language, and there is likely a smooth transition from one to the other. In fact, there isn't a "one" nor an "other" in a true qualitative sense.

      So what if we discover something about the human that appears to be required for our language? Does this, then, lead us to knowledge of how human language is qualitatively different from other languages?

      Can probabilistic models account for qualitative differences? If a very low, but not 0, probability is assigned to a given event that we know is impossible from our theory-based view, that doesn't make our probabilistic model useless. "All models are wrong, some are useful." But it seems that it does carry with it an assumption that there are no real categories, that categories change according to the needs, and are only useful in describing things. But the underlying nature of reality is of a continuum.

  8. jasss.soc.surrey.ac.uk jasss.soc.surrey.ac.uk
    1. To Guide Data Collection

      This seems to be, essentially, that models are useful for prediction, but prediction of unknowns in the data instead of prediction of future system dynamics.

    2. Without models, in other words, it is not always clear what data to collect!

      Or how to interpret that data in the light of complex systems.

    3. Plate tectonics surely explains earthquakes, but does not permit us to predict the time and place of their occurrence.

      But how do you tell the value of an explanation? Should it not empower you to some new action or ability? It could be that the explanation is somewhat of a by-product of other prediction-making theories (like how plate tectonics relies on thermodynamics, fluid dynamics, and rock mechanics, which do make predictions).

      It might also make predictions itself, such as that volcanoes not on clear plate boundaries might be somehow different (distribution of occurrence over time, correlation with earthquakes, content of magma, size of eruption...), or that understanding the explanation for lightning allows prediction that a grounded metal pole above the house might protect the house from lightning strikes. This might be a different kind of prediction, though, since it isn't predicting future dynamics. Knowing how epidemics works doesn't necessarily allow prediction of total infected counts or length of infection, but it does allow prediction of minimum vaccination rates to avert outbreaks.

      Nonetheless, a theory as a tool to explain, with very poor predictive ability, can still be useful, though less valuable than one that also makes testable predictions.

      But in general, it seems like data -> theory is the explanation. Theory -> data is the prediction. The strength of the prediction depends on the strength of the theory.

    1. Theseunderstandings of spatial technologies build on les-sons from science and technology studies (STS)research that describes the processes by which dataand technologies come to assume and reify social andpower relations, worldviews, and epistemologies(Feenberg1999; Pinch and Bijker1987; Wajcman1991; Winner1985)

      Good summation of Bijker's and Winner's STS work

    1. Unfortunately, there were more cases in 2018 than in 2017 (29 versus 22).

      The numbers and rosy picture here aren't quite as nice as other—more detailed—reporting in the Economist recently would lead us to believe.

      In some sense I do appreciate the sophistication of Bill Gates' science communication here though as I suspect that far more Westerners are his audience and a much larger proportion of them are uninformed anti-vaxxers who might latch onto the idea of vaccine-derived polio cases as further evidence for their worldview of not vaccinating their own children and thereby increasing heath risk in the United States.

    1. the Frauchiger-Renner paper when it first appeared on arxiv.org. In that version of the paper, the authors favored the many-worlds scenario. (The latest version of the paper, which was peer reviewed and published in Nature Communications in September, takes a more agnostic stance.

      I really love it when articles about science papers actually reference and link the original papers!

  9. Dec 2018
    1. Le commerce de l’échange savant dont les règles, les formes et les lieux peuvent être mis en cartes produit diverses sortes de validations qui permettent à leurs bénéficiaires d’entrer dans la négociation de situations matérielles : l’expression République des Lettres couvre, et mêle tout à la fois ces formes, ces lieux et un bon nombre de ces situations. Alors que l’échange et la validation des savoirs par les institutions académiques sont soumis à des conditions d’accès étroites et à des délais de publication encore plus longs pour les mémoires reçus par les sociétés que pour ceux de leurs propres membres, les périodiques savants s’ouvrent à des contributions d’origines très diverses qu’ils publient rapidement.

      cohabitation et complémentarité des formes de communication savante (voir l'intervention de Judith). Le périodique apparaît comme une ouverture.

    1. His weak-tie networks had been politically activated

      This makes me wonder if she's cited Mark Granovetter or any of similar sociologists yet?

      Apparently she did in footnote 32 in chapter 1. Ha!

    2. Only a segment of the population needs to be connected digitally to affect the entire environment. In Egypt in 2011, only 25 percent of the population of the country was on-line, with a smaller portion of those on Facebook, but these people still managed to change the wholesale public discussion, including conversa-tions among people who had never been on the site.

      There's some definite connection to this to network theory of those like Stuart Kaufmann. You don't need every node to be directly connected to create a robust network, particularly when there are other layers--here interpersonal connections, cellular, etc.

    1. Our under-standing of the gap is driven by technological exploration through artifact cre-ation and deployment, but HCI and CSCW systems need to have at their corea fundamental understanding of how people really work and live in groups, or-ganizations, communities, and other forms of collective life. Otherwise, wewill produce unusable systems, badly mechanizing and distorting collabora-tion and other social activity.

      The risk of CSCW not driving toward a more scientific pursuit of social theory, understanding, and ethnomethodology and instead simply building "cool toys"

    2. The gap is also CSCW’s unique contribution. CSCW exists intellectually atthe boundary and interaction of technology and social settings. Its unique intel-lectual importance is at the confluence of technology and the social, and its

      CSCW's potential to become a science of the artificial resides in the study of interactions between society and technology

    3. Nonetheless, several guiding questions are required based on thesocial–technical gap and its role in any CSCW science of the artificial:• When can a computational system successfully ignore the need fornuance and context?• When can a computational system augment human activity withcomputer technologies suitably to make up for the loss in nuance andcontext, as argued in the approximation section earlier?• Can these benefits be systematized so that we know when we are add-ing benefit rather than creating loss?• What types of future research will solve some of the gaps betweentechnical capabilities and what people expect in their full range of so-cial and collaborative activities?

      Questions to consider in moving CSCW toward a science of the artificial

    4. The final first-order approximation is the creation of technical architecturesthat do not invoke the social–technical gap; these architectures neither requireaction nor delegate it. Instead, these architectures provide supportive oraugmentative facilities, such as advice, to users.

      Support infrastructures provide a different type of approximation to augment the user experience.

    5. Another approximation incorporates new computational mechanisms tosubstitute adequately for social mechanisms or to provide for new social issues(Hollan & Stornetta, 1992).

      Approximate a social need with a technical cue. Example in Google Docs of anonymous user icons on page indicates presence but not identity.

    6. First-order approximations, to adopt a metaphor from fluid dynamics, aretractable solutions that partially solve specific problems with knowntrade-offs.

      Definition of first-order approximations.

      Ackerman argues that CSCW needs a set of approximations that drive the development of initial work-arounds for the socio-technical gaps.

      Essentially, how to satisfy some social requirements and then approximate the trade-offs. Doesn't consider the product a solution in full but something to iterate and improve

      This may have been new/radical thinking 20 years ago but seems to have been largely adopted by the CSCW community

    7. Similarly, an educational perspective would argue that programmers andusers should understand the fundamental nature of the social requirements.

      Ackerman argues that CS education should include understanding how to design/build for social needs but also to appreciate the social impacts of technology.

    8. CSCW’s science, however, must centralize the necessary gap between whatwe would prefer to construct and what we can construct. To do this as a practi-cal program of action requires several steps—palliatives to ameliorate the cur-rent social conditions, first-order approximations to explore the design space,and fundamental lines of inquiry to create the science. These steps should de-velop into a new science of the artificial. In any case, the steps are necessary tomove forward intellectually within CSCW, given the nature of the social–tech-nical gap.

      Ackerman sets up the steps necessary for CSCW to become a science of the artificial and to try to resolve the socio-technical gap:

      Palliatives to ameliorate social conditions

      Approximations to explore the design space

      Lines of scientific inquiry

    9. Ideological initiatives include those that prioritize the needs of the peopleusing the systems.

      Approaches to address social conditions and "block troublesome impacts":

      Stakeholder analysis

      Participatory design

      Scandinavian approach to info system design requires trade union involvement

    10. Simon’s (1969/1981) book does not address the inevitable gaps betweenthe desired outcome and the means of producing that outcome for anylarge-scale design process, but CSCW researchers see these gaps as unavoid-able. The social–technical gap should not have been ignored by Simon.Yet, CSCW is exactly the type of science Simon envisioned, and CSCW couldserve as a reconstruction and renewal of Simon’s viewpoint, suitably revised. Asmuch as was AI, CSCW is inherently a science of the artificial,

      How Ackerman sees CSCW as a science of the artificial:

      "CSCW is at once an engineering discipline attempting to construct suitable systems for groups, organizations, and other collectivities, and at the same time, CSCW is a social science attempting to understand the basis for that construction in the social world (or everyday experience)."

    11. At a simple level,CSCW’s intellectual context is framed by social constructionism andethnomethodology (e.g., Berger & Luckmann, 1966; Garfinkel, 1967), systemstheories (e.g., Hutchins, 1995a), and many large-scale system experiences (e.g.,American urban renewal, nuclear power, and Vietnam). All of these pointed tothe complexities underlying any social activity, even those felt to be straightfor-ward.

      Succinct description of CSCW as social constructionism, ethnomethodlogy, system theory and large-scale system implementation.

    12. Yet,The Sciences of the Artificialbecame an an-them call for artificial intelligence and computer science. In the book he ar-gued for a path between the idea for a new science (such as economics orartificial intelligence) and the construction of that new science (perhaps withsome backtracking in the creation process). This argument was both charac-teristically logical and psychologically appealing for the time.

      Simon defines "Sciences of the Artificial" as new sciences/disciplines that synthesize knowledge that is technically or socially constructed or "created and maintained through human design and agency" as opposed to the natural sciences

    13. The HCI and CSCW research communitiesneed to ask what one might do to ameliorate the effects of the gap and to fur-ther understand the gap. I believe an answer—and a future HCI challenge—is toreconceptualize CSCW as a science of the artificial. This echoes Simon (1981)but properly updates his work for CSCW’s time and intellectual task.2

      Ackerman describes "CSCW as a science of the artificial" as a potential approach to reduce the socio-technical gap

    1. A female student taking a math test experiences an extra cognitive and emotional burden of worry related to the stereotype that women are not good at math.

      If this is part of the probl;em how do you solve it? why doesnt it effect other careers

    1. Orbiting instruments are now so small they can be launched by the dozens, and even high school students can build them.

      A great way to get students interested in science as a career.

    1. The underrepresentation of girls and women in science, technology, engineering and mathematics (STEM) fields occurs globally.

      Where exactly globally?

  10. Nov 2018
    1. At a time of once-in-a-generation reform to healthcare in this country, the leaders of HM can’t afford to rest on their laurels, says Dr. Goldman. Three years ago, he wrote a paper for the Journal of Hospital Medicine titled “An Intellectual Agenda for Hospitalists.” In short, Dr. Goldman would like to see hospitalists move more into advancing science themselves rather than implementing the scientific discoveries of others. He cautions anyone against taking that as criticism of the field. “If hospitalists are going to be the people who implement what other people have found, they run the risk of being the ones who make sure everybody gets perioperative beta-blockers even if they don’t really work,” he says. “If you want to take it to the illogical extreme, you could have people who were experts in how most efficiently to do bloodletting. “The future for hospitalists, if they’re going to get to the next level—I think they can and will—is that they have to be in the discovery zone as well as the implementation zone.” Dr. Wachter says it’s about staying ahead of the curve. For 20 years, the field has been on the cutting edge of how hospitals treat patients. To grow even more, it will be crucial to keep that focus.

      Hospitalists can learn these skills through residency and fellowship training. In addition, through mentorship models that create evergrowing

    1. It’s important to remember that utopia and dystopia aren’t the only terms here. You need to use the Greimas rectangle and see that utopia has an opposite, dystopia, and also a contrary, the anti-utopia. For every concept there is both a not-concept and an anti-concept. So utopia is the idea that the political order could be run better. Dystopia is the not, being the idea that the political order could get worse. Anti-utopias are the anti, saying that the idea of utopia itself is wrong and bad, and that any attempt to try to make things better is sure to wind up making things worse, creating an intended or unintended totalitarian state, or some other such political disaster. 1984 and Brave New World are frequently cited examples of these positions. In 1984 the government is actively trying to make citizens miserable; in Brave New World, the government was first trying to make its citizens happy, but this backfired. As Jameson points out, it is important to oppose political attacks on the idea of utopia, as these are usually reactionary statements on the behalf of the currently powerful, those who enjoy a poorly-hidden utopia-for-the-few alongside a dystopia-for-the-many. This observation provides the fourth term of the Greimas rectangle, often mysterious, but in this case perfectly clear: one must be anti-anti-utopian.
    2. For a while now I’ve been saying that science fiction works by a kind of double action, like the glasses people wear when watching 3D movies. One lens of science fiction’s aesthetic machinery portrays some future that might actually come to pass; it’s a kind of proleptic realism. The other lens presents a metaphorical vision of our current moment, like a symbol in a poem. Together the two views combine and pop into a vision of History, extending magically into the future. By that definition, dystopias today seem mostly like the metaphorical lens of the science-fictional double action. They exist to express how this moment feels, focusing on fear as a cultural dominant. A realistic portrayal of a future that might really happen isn’t really part of the project—that lens of the science fiction machinery is missing. The Hunger Games trilogy is a good example of this; its depicted future is not plausible, not even logistically possible. That’s not what it’s trying to do. What it does very well is to portray the feeling of the present for young people today, heightened by exaggeration to a kind of dream or nightmare. To the extent this is typical, dystopias can be thought of as a kind of surrealism.
    3. These days I tend to think of dystopias as being fashionable, perhaps lazy, maybe even complacent, because one pleasure of reading them is cozying into the feeling that however bad our present moment is, it’s nowhere near as bad as the ones these poor characters are suffering through. Vicarious thrill of comfort as we witness/imagine/experience the heroic struggles of our afflicted protagonists—rinse and repeat. Is this catharsis? Possibly more like indulgence, and creation of a sense of comparative safety. A kind of late-capitalist, advanced-nation schadenfreude about those unfortunate fictional citizens whose lives have been trashed by our own political inaction. If this is right, dystopia is part of our all-encompassing hopelessness. On the other hand, there is a real feeling being expressed in them, a real sense of fear. Some speak of a “crisis of representation” in the world today, having to do with governments—that no one anywhere feels properly represented by their government, no matter which style of government it is. Dystopia is surely one expression of that feeling of detachment and helplessness. Since nothing seems to work now, why not blow things up and start over? This would imply that dystopia is some kind of call for revolutionary change. There may be something to that. At the least dystopia is saying, even if repetitiously and unimaginatively, and perhaps salaciously, Something’s wrong. Things are bad.
    1. Freedom of intramural expression means that teaching personnel is not only allowed to teach according to their knowledge, but that they can take part in the administration of their institutions. This is supported by the freedom of extramural expression, which gives teachers the capacity to share their research outcomes and disseminate the knowledge acquired.

      participation in activities to share research outcomes.

    1. Researchers now typically engage in a range of ‘questionable research practices’ in the hunt for the glory of publication, with such conditions leading to mental health issues in a higher proportion than any other industry.  

      'publish or perish' culture creating mental health issues.

    1. Unless you need to push the boundaries of what these technologies are capable of, you probably don’t need a highly specialized team of dedicated engineers to build solutions on top of them. If you manage to hire them, they will be bored. If they are bored, they will leave you for Google, Facebook, LinkedIn, Twitter, … – places where their expertise is actually needed. If they are not bored, chances are they are pretty mediocre. Mediocre engineers really excel at building enormously over complicated, awful-to-work-with messes they call “solutions”. Messes tend to necessitate specialization.
    1. At the same time, we now have several years of experience launching and running new and innovative publications in broad fields. For example, PeerJ – the Journal of Life & Environmental Sciences covers all of biology, the life sciences, and the environmental sciences in a single title; whilst PeerJ Computer Science is targeted towards a more well-defined community. In 2013 we also launched a preprint server (PeerJ Preprints) which covers all the areas in which we publish; and we have developed a comprehensive suite of journal and peer-review functionalities.

      New journals released by PeerJ