Social media research ethics faces a contradiction between big data positivism and research ethics fundamentalism. Big data positivists tend to say, ‘Most social media data is public data. It is like data in a newspaper. I can therefore gather big data without limits. Those talking about privacy want to limit the progress of social science’. This position disregards any engagement with ethics and has a bias towards quantification. The ethical framework Social Media Research: A Guide to Ethics (Townsend and Wallace, 2016) that emerged from an ESRC-funded project tries to avoid both extremes and to take a critical-realist position: It recommends that social scientists neither ignore nor fetishize research ethics when studying digital media.Research ethics fundamentalists in contrast tend to say,You have to get informed consent for every piece of social media data you gather because we cannot assume automatic consent, users tend not to read platform’s privacy policies, they may assume some of their data is private and they may not agree to their data being used in research. Even if you anonymize the users you quote, many can still be identified in the networked online environment.

Research ethics concerns issues, such as privacy, anonymity, informed consent and the sensitivity of data. Given that social media is part of society’s tendency to liquefy and blur the boundaries between the private and the public, labour/leisure, production/consumption (Fuchs, 2015a: Chapter 8), research ethics in social media research is par-ticularly complex.

One important aspect of critical social media research is the study of not just ideolo-gies of the Internet but also ideologies on the Internet. Critical discourse analysis and ideology critique as research method have only been applied in a limited manner to social media data. Majid KhosraviNik (2013) argues in this context that ‘critical dis-course analysis appears to have shied away from new media research in the bulk of its research’ (p. 292). Critical social media discourse analysis is a critical digital method for the study of how ideologies are expressed on social media in light of society’s power structures and contradictions that form the texts’ contexts.

Besides conducting qualitative social research with social media users in order to learn about their experiences, interpretations and perspectives, critical digital methods should not completely discard tools for digital data collection and analytics but take their use into a new direction. Critical digital methods should certainly engage in collecting and analys-ing samples of data from social media platforms with the help of tools and services, such as DiscoverText, Tweet Archivist, Netvizz, NodeXL, Gephi, NCapture/NVivo, Sodato, Import.io, InfoExtractor, Google Web Scraper, TAGS, SocioViz and so on.

t has, for example, been common to study contemporary revolutions and protests (such as the 2011 Arab Spring) by collecting large amounts of tweets and analysing them. Such analyses can, however, tell us nothing about the degree to which activists use social and other media in protest communication, what their motivations are to use or not use social media, what their experiences have been, what problems they encounter in such uses and so on. If we only analyse big data, then the one-sided conclusion that con-temporary rebellions are Facebook and Twitter revolutions is often the logical conse-quence (see Aouragh, 2016; Gerbaudo, 2012). Digital methods do not outdate but require traditional methods in order to avoid the pitfall of digital positivism. Traditional socio-logical methods, such as semi-structured interviews, participant observation, surveys, content and critical discourse analysis, focus groups, experiments, creative methods, par-ticipatory action research, statistical analysis of secondary data and so on, have not lost importance. We do not just have to understand what people do on the Internet but also why they do it, what the broader implications are, and how power structures frame and shape online activities

Studying digital and social media could take inspiration by the tradition going back to Karl Marx and other critical theorists

Who communicates what to whom on social media with what effects? It forgets users’ subjectivity, experiences, norms, values and interpre-tations, as well as the embeddedness of the media into society’s power structures and social struggles. We need a paradigm shift from administrative digital positivist big data analytics towards critical social media research. Critical social media research combines critical social media theory, critical digital methods and critical-realist social media research ethics.

de-emphasis of philosophy, theory, critique and qualitative analysis advances what Paul Lazarsfeld (2004 [1941]) termed administrative research, research that is predominantly concerned with how to make technologies and administration more efficient and effective.

There is a tendency in Internet Studies to engage with theory only on the micro- and middle-range levels that theorize single online phenomena but neglect the larger picture of society as a totality (Rice and Fuller, 2013). Such theories tend to be atomized. They just focus on single phenomena and miss soci-ety’s big picture

scientific laboratories create an amazing amount of plastic waste, consume large amounts of water, create risks from hazardous chemicals and use significantly more energy

I use this to keep information about processes that were running at any time during last five days. These are 1-min snapshots so something might get lost but I think it is good enough for me. I want to have some data available when I discover there was a peak in resource usage (I use munin for that). I haven't found a better way to keep track of past processes (tried psacct).

0:11 / 12:00LiveScroll for details M2 Research Categorization

Summarizing a paper in your own words restructures the content to focus on learning rather than novelty.

Blogging has taught me how to read a paper because explaining something is a more active form of understanding. Now I summarize the main contribution in my own words, write out the notation and problem setup, define terms, and rederive the main equations or results. This process mimics the act of presenting and is great practice for it.

When I first started teaching myself to program, I felt that I had no imagination. I couldn’t be creative because I was too focused on finding the syntax bug or reasoning about program structure. However, with proficiency came creativity. Programming became less important than what I was building and why.

In my opinion the reason most people fail to do great research is that they are not willing to pay the price in self-development. Say some new field opens up that combines field XXX and field YYY. Researchers from each of these fields flock to the new field. My experience is that virtually none of the researchers in either field will systematically learn the other field in any sort of depth. The few who do put in this effort often achieve spectacular results.

Many of us have done this on exams, hoping for partial credit by stitching together the outline of a proof or using the right words in an essay with the hopes that the professor connects the dots for us.

L-T3 has proven to be 4-5 times more biologically active and to take effect more quickly than L-thyroxine (L-T4).

In almost all cases the genetic basis of RTH lies in mutation of the carboxyl-terminus of the ß-thyroid hormone receptor. RTH is a dominant disorder, except in one family; most individuals are heterozygous for the mutant allele.

Wikipedia:Articles for deletion/Katie Bouman

Any relevant material can be mentioned there

of WP:1E

Someone who isn't even an assistant professor is certainly not notable as a scientist.

Wikipedia:Notability is not inherited.

The Event Horizon Telescope project is notable in itself, and has its own article, but anyone who are in some way (remotely) associated with it are not inherently notable.

Wikipedia:Articles for deletion/Katie Bouman • en.wikipedia.org

Through a process called rehashing, rbenv maintains shims

The Research Parasite Awards

2019 Research Parasite Awards

One of the key critiques of the study is that the researchers didn’t log in. That is to say that they could not experience the full impact of the algorithm as it impacts their findings.

OPP1151904

Fourth, inoffering examples of how the tools have been used, I encouragethought about how the tools might be used in other educationalresearchers’ work

A 2009 study of Wikipedia found that most weasel words in it could be divided into three main categories:[13] Numerically vague expressions (for example, "some people", "experts", "many", "evidence suggests") Use of the passive voice to avoid specifying an authority (for example, "it is said") Adverbs that weaken (for example, "often", "probably")

EPush Notifications

The sodium-restricted diet group received a regimen aiming a maximum intake of 3 g of sodium per day (equivalent to 7.5 g of sodium chloride).

This repo attempts to show how neutrino config can be used to configure storybook

“Why don’t you assume you’ve written your book already — and all you have to do now is find it?”

Here’s Stephen Harrigan (talking about his book, Big Wonderful Thing: A History of Texas)

In my experience writing books, it isn’t just a “resistance” thing or a “perfectionist” thing or a fear thing, it’s more about research and wondering if you’ve done enough of it. Research becomes your way of procrastinating, because, let’s face it, research is just more fun than writing. (Me, personally, I became a professional writer so I could be a professional reader.)

There’s an awful temptation to just keep on researching. There comes a point where you just have to stop, and start writing. When I began, I thought that the way one should work was to do all the research and then write the book. In time I began to understand that it’s when you start writing that you really find out what you don’t know and need to know.

Wchodziło długo, prawdziwie odurzony byłem po godzinie. Stan, w którym się znalazłem można określić jednym słowem, ale pisanym koniecznie z wielkiej litery: Spokój. Wielki Spokój, jakby nic złego nigdy się nie działo, jakby moje wnętrze było boską harmonią, idealnie zestrojoną z zachwycającym światem wokół. Myślałem i mówiłem bardzo trzeźwo: być może tak trzeźwo, jak nigdy: wyzbyty bowiem byłem z najmniejszych śladów niepewności, wstydu, nerwów, kompleksów... Było tak, jakbym znalazł w umyśle właściwe drzwiczki, tej najwygodniejszy pokój i rozłożył się w nim, delektując się (w nienarcystyczny sposób) sobą, swoim byciem i byciem, które mnie otaczało: świata, przedmiotów, światła, ludzi, Krzyśka, Andrzeja. Wszystko było dobre, a jeśli nie było - nie było godne uwagi. Zdumiewała mnie refleksja nad idiotyzmem wielkiej części moich codziennych zmartwień, wszystkich tych mniejszych i większych narcyzmów, skupienia na naskórku rzeczywistości. Andrzej w międzyczasie znalazł się w bardzo podobnym stanie, tylko, że połączonym z OEVami (opened eyes' visuals), do tego mówił dużo, gestykulując i intonując jak dziecko: "Przecież to wszystko takie proste...! Takie proste! A ludzie się przejmują!".

Units of analysis, then, are those things we examine in arder to create summary descriptions of ali such units and to explain differences among them

Units of Analysis

A given variable can sornetimes be measured at different levels. When in doubt, researchers should use the highest leve! of rneasurement ap-propriate to that variable so they can capture the greatest amount of information.

The Importance of Variable Names

Conceptions, Concepts, and Reality

Checklist: Theoretical Framework

Concepts often have multiple definitions, so the theoretical framework involves clearly defining what you mean by each term

In your thesis or dissertation, the theoretical framework is where you define, discuss and evaluate theories relevant to your research problem.

Sample theoretical framework

recruiting volunteers

What is a mystical experience?

Conceptualization is the process of specifying observations and measurements that give concepts definite meaning for the purposes of a research study.

Concepts are constructs; they represent the agreed-on meanings we assign to terms

Concepts are mental irnages we use as sumrnary devices for bringing together observations and experiences that seem to have something in corn-mon. We use terms or labels to reference these concepts

Conceptualization, Operationalization, and Measurement

Research Design

The theoretical framework: what and how?

Abstract

It is generally agreed that literature surveys and descriptive compilations do not meet the contribution-to-knowledge re-quirement for the dissertation

Positivist versus postpositivist.

Experimental versus descriptive.

Quantitative versus qualitative.

NEW OR IMPROVED ANALYSIS Analysis may be based on existing evidence or include new data.

The evidence may be collected by an experiment, simulation, observations, questionnaire, interviews, or measurements.

The additive contribution of a dissertation may arise from 1. new or improved evidence; 2. new or improved methodology; 3. new or improved analysis; 4. new or improved concepts or theories.

The dissertation should be based on a significant question, problem, or hypothesis.

Different approaches to testing of important results. If a researcher has reported interesting results with one research technique and a given research population, a doctoral student may consider replicating the experiment, altering either the research technique or the research population.

Writers of disserta-tions commonly describe further research that needs to be done.

If there is likely to be a continuing interest, either academically or otherwise on the topic, then a student can continué to maintin scholarly capability in the área and continué to be a significant authority on the subject.

A research project will typicaliy have more than one potential outcome. For example, a research experiment may fail to dis-prove the nuil hypothesis, it may disprove it, or it may be incon-clusive.

The exploratory investigation, definition of problem, and writing normally take about half of the total time.

If no theory base can be identified, the topic should be rejected

Observations lead to theory to classify, explain, and predict the observations.

Research needed and interesting

In reading dissertations, the student should begin to formúlate a general understanding of the structure and scope of a disserta-tion, and the meaning of contribution to knowledge as applied to doctoral dissertations.

The Selection of a Dissertation Topic

INTRODUCTION

. To summarize: Your aim is to explain 1. what you are writing about —I am working on the topic of... 2. what you don't know about it—because / want tofind out... 3. why you want your reader to know and care about it—m order to help my reader understand better...

add a second indirect question that explains why you asked your first question.

When you add that because I want tofind out how/why/whether clause, you state why you are pursuing your topic: to answer a question important to you.

because I want to find out who/what/when/where/whether/ why/how .

start by naming your project:

SO WHAT?

If you are an experienced researcher, look for questions that other researchers ask but don't answer.

How does your topic fit into the context of a larger structure or function as part of a larger system?

Ask about the History of Your Topic

So the best way to begin working on your focused topic is not to find all the information you can on it, but to formúlate questions that direct you to just that information you need to answer them

If a writer asks no specific question worth asking, he can offer no specific answer worth supporting.

Caution: Don't narrow your topic so much that you can't find information on it

We narrowed those topics by adding words and phrases, but of a special kind: conflict, description, contribution, and developing. Those nouns are derived from verbs expressing actions or relation-ships: to conflict, to describe, to contribute, and to develop. Lacking such "action" words, your topic is a .static thing.

A topic is probably too broad if you can state it in four or five words

Few experi-enced researchers trust Wikipedia, so under no circumstances cite it as a source of evidence (unless your topic is Wikipedia itself).

Google your topic,

Once you have a list of topics, choose the one or two that inter-est you most and explore their research potential. Do this:

Start by listing as many interests as you can that you'd like to explore.

But also ask yourself: What interests me about this tapie? What would interest others?

Some questions raise problems; others do not.

But other questions may intrigue only the researcher:

A subject is a broad área of knowledge (e.g., climate change), while a topic is a specific interest within that área (e.g., the effect of climate change on migratory birds).

As you begin a research project, you will want to distinguish a topic from a sub-ject.

From Topics to Questions

All of your main points are contained in the body, and normally this section is prepared well before you ever write the introduction or conclusion. The body of your speech will consume the largest amount of time to present; and it is the opportunity for you to elaborate on facts, evidence, examples, and opinions that support your thesis statement and do the work you have outlined in the specific purpose statement.

Shonda

Estimating the Effect of Asking About Citizenship on the U.S. Census March 21, 2019, 1:21 pm

Research Design

JOANNE CONROY

ce. We conclude with a discussion of directions for future research aimed at incorporating comments in the content design pro-cess and for enhancing the user’s experience via new design features in commenting platforms.

Moreover, annotation is the agreed upon means of starting and sustaining that conversation.

computational techniques can improve upon and enhance existingapproaches, providing more efficient ways of identifying some typesof anomalies and providing a historical picture of the evolution oflanguage and activity over time

Current theories on aging suggest that changes in heterochromatin may be responsible for age-related changes in gene expression and alterations in H3K9me3 may function as a driver of aging

Their work demonstrates a method of quickly “reading” whole words and phrases from the brain — getting Facebook slightly closer to its dream of a noninvasive thought-typing system.

One reason for this is publication bias: journal editors, reviewers and authors can favour positive, sometimes eye-catching results over negative findings. But negative results waste research funds and researchers’ time if they do not reach the research community through publication, and, in the case of clinical studies, can lead to fruitless patient involvement.

The scientists were astonished by the results: selective noradrenaline release re-wired the connectivity patterns between different brain regions in a way that was extremely similar to the changes observed in humans exposed to acute stress. Networks that process sensory stimuli, such as the visual and auditory center of the brain, exhibited the strongest increase in activity. A similar rise in activity was observed in the amygdala network, which is associated with states of anxiety.

Schools can ensure their curriculum are up to date and training students for the areas of science with the most potential for advancement

Funding organizations like universities and foundations can get in touch with authors to back their future work, or spot trends of where breakthroughs are being made so they can funnel resources correctly

academics often approach books like “sous-chefs gutting a fish.”

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.

When geneticists finally gained the power to cost-efficiently analyze entire genomes, they realized that most disorders and diseases are influenced by thousands of genes, each of which has a tiny effect. To reliably detect these miniscule effects, you need to compare hundreds of thousands of volunteers. By contrast, the candidate-gene studies of the 2000s looked at an average of 345 people!

It’s as if they’d been “describing the life cycle of unicorns, what unicorns eat, all the different subspecies of unicorn, which cuts of unicorn meat are tastiest, and a blow-by-blow account of a wrestling match between unicorns and Bigfoot,” Alexander wrote.

“How on Earth could we have spent 20 years and hundreds of millions of dollars studying pure noise?”

Faculty members are increasingly interested in open access publication models. Approximately 64%

stressful but fascinating

While I would say that Alexander L. George and Andrew Bennett’s book “Case Studies and Theory Development in the Social Sciences“, is neither a new book nor an old one (it was published in 2004), it is definitely a classic and a must-read. Moreover, I’m a comparativist, and someone who undertakes systematic case study comparisons, so George and Bennett’s book is definitely my go-to when I want to revise my research strategy.

But thirdly, and most valuably, the template gives you a big space at the bottom to write sentences that summarise the page.  That is, you start writing your critical response on the notes themselves.

Eine beliebte Klassifizierung dynamischer Eigenschaften liefert die Unterscheidung inSicherheitsbedin-gungen(safety conditions) auf der eine Seite undLebendigkeitsbedingungen(liveness conditions) auf deranderen Seite. Nach [14], S. 94, schließt eine Sicherheitsbedingung das Auftreten von etwas Schlechtem aus, wäh-rend eine Lebendigkeitsbedingung das Auftreten von etwas Gutem garantiert. E

This aspect has led some to claim practitioner research shares many qualities of social movements (e.g., Campano, 2009; Cochran-Smith & Lytle, 2009).

Svante Arrhenius in 1896. His paper, published in the Philosophical Magazine and Journal of Science, was the first to quantify the contribution of carbon dioxide to what scientists now call the "greenhouse effect."

Measuring the impact of a new faculty program using institutional data

The more recent the research is, the better—or at least, more relevant—it’s assumed to be.

Data journalism produced by two of the nation’s most prestigious news organizations — The New York Times and The Washington Post — has lacked transparency, often failing to explain the methods journalists or others used to collect or analyze the data on which the articles were based, a new study finds. In addition, the news outlets usually did not provide the public with access to that data

Worse yet, it wouldn’t surprise me if we saw more unethical people publish data as a strategic communication tool, because they know people tend to believe numbers more than personal stories. That’s why it’s so important to have that training on information literacy and methodology.”

However, when asked if they would be willing to participate in action research teaming in the future, preservice teacher candidates were more positive (x̄=6.4)<math><mtext>(</mtext><mtext>x</mtext><mtext>̄</mtext><mtext>=6.4)</mtext></math> than their veteran counterparts (x̄=5.8)<math><mtext>(</mtext><mtext>x</mtext><mtext>̄</mtext><mtext>=5.8)</mtext></math>.

One way to overcome this isolation is to encourage collaboration with informed peers through established frameworks within school communities.

Having the expertise and context of the entire team in the room – the product designer, product manager and the engineers – means the plans are holistic and viable rather than limited to the lens of one of the roles

This method allows us to learn before shipping and make sure that we can be more responsible with the design decisions we make

The matrix reduces the role of bias while giving space for other findings to emerge

Focusses on what the user does, not what they say or think

User research is about reducing uncertainty in the design process

Another way of thinking about a good metric is to define a bad one. Bad metrics include those that are:

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

to report findings openly and accurately, this includes in particular (where feasible and appropriate) to report findings back to the participants and communities who have engaged in the work, in a form and in language that is useful and accessible for the participants and partners involved;

These discussions can be fraught with power dynamics, resulting in controversial issues appearing unbalanced as more powerful authors block alternative viewpoints.

Creating mental and physical representations of digital content focused on accessibility and approachability

No, I started the research, read a little bit about algorithmic trading in looking at different strategies, but I have not developed anything. Just just been researching, reading more about how people do..what Indicators, you know, people are using. Although most of the research I've been doing and one of the books I read was on the equities or algorithms, but they're all based technically so you can interchange markets within them. You just have to tweak whatever your strategy is to match what, what particular market

So it's not to say I don't read them, but it's not necessarily something that I trade on some other guy's opinion.