Armchair Survey Research: A Possible Post-COVID-19 Boon in Social Science

Curated on Posted on by Stefaan Verhulst

Paper by Samiul Hasan: “Post-COVID-19 technologies for higher education and corporate communication have opened-up wonderful opportunity for Online Survey Research. These technologies could be used for one-to-one interview, group interview, group questionnaire survey, online questionnaire survey, or even ‘focus group’ discussions. This new trend, which may aptly be called ‘armchair survey research’ may be the only or new trend in social science research. If that is the case, an obvious question might be what is ‘survey research’ and how is it going to be easier in the post-COVID-19 world? My intention is to offer some help to the promising researchers who have all quality and eagerness to undertake good social science research for publication, but no fund.

The text is divided into three main parts. Part one deals with “Science, Social Science and Research” to highlight some important points about the importance of ‘What’, ‘Why’, and ‘So what’ and ‘framing of a research question’ for a good research. Then the discussion moves to ‘reliability and validity’ in social science research including falsifiability, content validity, and construct validity. This part ends with discussions on concepts, constructs, and variables in a theoretical (conceptual) framework. The second part deals categorically with ‘survey research’ highlighting the use and features of interviews and questionnaire surveys. It deals primarily with the importance and use of nominal response or scale and ordinal response or scale as well as the essentials of question content and wording, and question sequencing. The last part deals with survey research in the post-COVID-19 period highlighting strategies for undertaking better online survey research, without any fund….(More)”.

Scaling up Citizen Science

Curated on Posted on by Stefaan Verhulst

Report for the European Commission: “The rapid pace of technology advancements, the open innovation paradigm, and the ubiquity of high-speed connectivity, greatly facilitate access to information to individuals, increasing their opportunities to achieve greater emancipation and empowerment. This provides new opportunities for widening participation in scientific research and policy, thus opening a myriad of avenues driving a paradigm shift across fields and disciplines, including the strengthening of Citizen Science. Nowadays, the application of Citizen Science principles spans across several scientific disciplines, covering different geographical scales. While the interdisciplinary approach taken so far has shown significant results and findings, the current situation depicts a wide range of projects that are heavily context-dependent and where the learning outcomes of pilots are very much situated within the specific areas in which these projects are implemented. There is little evidence on how to foster the spread and scalability in Citizen Science. Furthermore, the Citizen Science community currently lacks a general agreement on what these terms mean, entail and how these can be approached.

To address these issues, we developed a theoretically grounded framework to unbundle the meaning of scaling and spreading in Citizen Science. In this framework, we defined nine constructs that represent the enablers of these complex phenomena. We then validated, enriched, and instantiated this framework through four qualitative case studies of, diverse, successful examples of scaling and spreading in Citizen Science. The framework and the rich experiences allow formulating four theoretically and empirically grounded scaling scenarios. We propose the framework and the in-depth case studies as the main contribution from this report. We hope to stimulate future research to further refine our understanding of the important, complex and multifaceted phenomena of scaling and spreading in Citizen Science. The framework also proposes a structured mindset for practitioners that either want to ideate and start a new Citizen Science intervention that is scalable-by-design, or for those that are interested in assessing the scalability potential of an existing initiative….(More)”.

tl;dr: this AI sums up research papers in a sentence

Curated on Posted on by Stefaan Verhulst

Jeffrey M. Perkel & Richard Van Noorden at Nature: “The creators of a scientific search engine have unveiled software that automatically generates one-sentence summaries of research papers, which they say could help scientists to skim-read papers faster.

The free tool, which creates what the team calls TLDRs (the common Internet acronym for ‘Too long, didn’t read’), was activated this week for search results at Semantic Scholar, a search engine created by the non-profit Allen Institute for Artificial Intelligence (AI2) in Seattle, Washington. For the moment, the software generates sentences only for the ten million computer-science papers covered by Semantic Scholar, but papers from other disciplines should be getting summaries in the next month or so, once the software has been fine-tuned, says Dan Weld, who manages the Semantic Scholar group at AI2…

Weld was inspired to create the TLDR software in part by the snappy sentences his colleagues share on Twitter to flag up articles. Like other language-generation software, the tool uses deep neural networks trained on vast amounts of text. The team included tens of thousands of research papers matched to their titles, so that the network could learn to generate concise sentences. The researchers then fine-tuned the software to summarize content by training it on a new data set of a few thousand computer-science papers with matching summaries, some written by the papers’ authors and some by a class of undergraduate students. The team has gathered training examples to improve the software’s performance in 16 other fields, with biomedicine likely to come first.

The TLDR software is not the only scientific summarizing tool: since 2018, the website Paper Digest has offered summaries of papers, but it seems to extract key sentences from text, rather than generate new ones, Weld notes. TLDR can generate a sentence from a paper’s abstract, introduction and conclusion. Its summaries tend to be built from key phrases in the article’s text, so are aimed squarely at experts who already understand a paper’s jargon. But Weld says the team is working on generating summaries for non-expert audiences….(More)”.

Facial-recognition research needs an ethical reckoning

Curated on Posted on by Stefaan Verhulst

Editorial in Nature: “…As Nature reports in a series of Features on facial recognition this week, many in the field are rightly worried about how the technology is being used. They know that their work enables people to be easily identified, and therefore targeted, on an unprecedented scale. Some scientists are analysing the inaccuracies and biases inherent in facial-recognition technology, warning of discrimination, and joining the campaigners calling for stronger regulation, greater transparency, consultation with the communities that are being monitored by cameras — and for use of the technology to be suspended while lawmakers reconsider where and how it should be used. The technology might well have benefits, but these need to be assessed against the risks, which is why it needs to be properly and carefully regulated.Is facial recognition too biased to be let loose?

Responsible studies

Some scientists are urging a rethink of ethics in the field of facial-recognition research, too. They are arguing, for example, that scientists should not be doing certain types of research. Many are angry about academic studies that sought to study the faces of people from vulnerable groups, such as the Uyghur population in China, whom the government has subjected to surveillance and detained on a mass scale.

Others have condemned papers that sought to classify faces by scientifically and ethically dubious measures such as criminality….One problem is that AI guidance tends to consist of principles that aren’t easily translated into practice. Last year, the philosopher Brent Mittelstadt at the University of Oxford, UK, noted that at least 84 AI ethics initiatives had produced high-level principles on both the ethical development and deployment of AI (B. Mittelstadt Nature Mach. Intell. 1, 501–507; 2019). These tended to converge around classical medical-ethics concepts, such as respect for human autonomy, the prevention of harm, fairness and explicability (or transparency). But Mittelstadt pointed out that different cultures disagree fundamentally on what principles such as ‘fairness’ or ‘respect for autonomy’ actually mean in practice. Medicine has internationally agreed norms for preventing harm to patients, and robust accountability mechanisms. AI lacks these, Mittelstadt noted. Specific case studies and worked examples would be much more helpful to prevent ethics guidance becoming little more than window-dressing….(More)”.

Leveraging Open Data with a National Open Computing Strategy

Curated on Posted on by Stefaan Verhulst

Policy Brief by Lara Mangravite and John Wilbanks: “Open data mandates and investments in public data resources, such as the Human Genome Project or the U.S. National Oceanic and Atmospheric Administration Data Discovery Portal, have provided essential data sets at a scale not possible without government support. By responsibly sharing data for wide reuse, federal policy can spur innovation inside the academy and in citizen science communities. These approaches are enabled by private-sector advances in cloud computing services and the government has benefited from innovation in this domain. However, the use of commercial products to manage the storage of and access to public data resources poses several challenges.

First, too many cloud computing systems fail to properly secure data against breaches, improperly share copies of data with other vendors, or use data to add to their own secretive and proprietary models. As a result, the public does not trust technology companies to responsibly manage public data—particularly private data of individual citizens. These fears are exacerbated by the market power of the major cloud computing providers, which may limit the ability of individuals or institutions to negotiate appropriate terms. This impacts the willingness of U.S. citizens to have their personal information included within these databases.

Second, open data solutions are springing up across multiple sectors without coordination. The federal government is funding a series of independent programs that are working to solve the same problem, leading to a costly duplication of effort across programs.

Third and most importantly, the high costs of data storage, transfer, and analysis preclude many academics, scientists, and researchers from taking advantage of governmental open data resources. Cloud computing has radically lowered the costs of high-performance computing, but it is still not free. The cost of building the wrong model at the wrong time can quickly run into tens of thousands of dollars.

Scarce resources mean that many academic data scientists are unable or unwilling to spend their limited funds to reuse data in exploratory analyses outside their narrow projects. And citizen scientists must use personal funds, which are especially scarce in communities traditionally underrepresented in research. The vast majority of public data made available through existing open science policy is therefore left unused, either as reference material or as “foreground” for new hypotheses and discoveries….The Solution: Public Cloud Computing…(More)”.

Not fit for Purpose: A critical analysis of the ‘Five Safes’

Curated on Posted on by Stefaan Verhulst

Paper by Chris Culnane, Benjamin I. P. Rubinstein, and David Watts: “Adopted by government agencies in Australia, New Zealand, and the UK as policy instrument or as embodied into legislation, the ‘Five Safes’ framework aims to manage risks of releasing data derived from personal information. Despite its popularity, the Five Safes has undergone little legal or technical critical analysis. We argue that the Fives Safes is fundamentally flawed: from being disconnected from existing legal protections and appropriation of notions of safety without providing any means to prefer strong technical measures, to viewing disclosure risk as static through time and not requiring repeat assessment. The Five Safes provides little confidence that resulting data sharing is performed using ‘safety’ best practice or for purposes in service of public interest….(More)”.

Statistical illiteracy isn’t a niche problem. During a pandemic, it can be fatal

Curated on Posted on by Stefaan Verhulst

Article by Carlo Rovelli: “In the institute where I used to work a few years ago, a rare non-infectious illness hit five colleagues in quick succession. There was a sense of alarm, and a hunt for the cause of the problem. In the past the building had been used as a biology lab, so we thought that there might be some sort of chemical contamination, but nothing was found. The level of apprehension grew. Some looked for work elsewhere.

One evening, at a dinner party, I mentioned these events to a friend who is a mathematician, and he burst out laughing. “There are 400 tiles on the floor of this room; if I throw 100 grains of rice into the air, will I find,” he asked us, “five grains on any one tile?” We replied in the negative: there was only one grain for every four tiles: not enough to have five on a single tile.

We were wrong. We tried numerous times, actually throwing the rice, and there was always a tile with two, three, four, even five or more grains on it. Why? Why would grains “flung randomly” not arrange themselves into good order, equidistant from each other?

Because they land, precisely, by chance, and there are always disorderly grains that fall on tiles where others have already gathered. Suddenly the strange case of the five ill colleagues seemed very different. Five grains of rice falling on the same tile does not mean that the tile possesses some kind of “rice-­attracting” force. Five people falling ill in a workplace did not mean that it must be contaminated. The institute where I worked was part of a university. We, know-­all professors, had fallen into a gross statistical error. We had become convinced that the “above average” number of sick people required an explanation. Some had even gone elsewhere, changing jobs for no good reason.

Life is full of stories such as this. Insufficient understanding of statistics is widespread. The current pandemic has forced us all to engage in probabilistic reasoning, from governments having to recommend behaviour on the basis of statistical predictions, to people estimating the probability of catching the virus while taking part in common activities. Our extensive statistical illiteracy is today particularly dangerous.

We use probabilistic reasoning every day, and most of us have a vague understanding of averages, variability and correlations. But we use them in an approximate fashion, often making errors. Statistics sharpen and refine these notions, giving them a precise definition, allowing us to reliably evaluate, for instance, whether a medicine or a building is dangerous or not.

Society would gain significant advantages if children were taught the fundamental ideas of probability theory and statistics: in simple form in primary school, and in greater depth in secondary school….(More)”.

Responsive Science

Curated on Posted on by Stefaan Verhulst

Paper by Peter Drahos: “Regulatory capitalism depends heavily on science, but science faces epi-stemic critiques and crises of research integrity. These critiques and crises are outlined and then located within capitalism’s general tragedy of commodification. Drawing on Marx’s insights into the relationship between science, commodity production, and the machine age, the general tragedy of commodification is outlined. From here, the article shifts to discussing some well-known global public good problems relating to access to medicines and access to knowledge. The roots of these problems can be traced back to the way the institution of science has been bent toward processes of capital accumulation. The evidence we have from the history of science suggests that too often its research agendas have been set by capital and the demands of war-making capitalist states. The final part of the article considers whether the ideal of responsiveness might help us to reformulate the way in which we think about the responsibilities and duties of science. It focuses on human rights, citizen science, and the intellectual commons as potential sources of responsiveness. Responsiveness has been a fertile ideal for law and society theorists when it has come to theory building in law and regulation. It also has something to offer the debates around the crises of science….(More)”.

Science Philanthropy and Societal Responsibility: A Match Made for the 21st Century

Curated on Posted on by Stefaan Verhulst

Blog by Evan S. Michelson: “The overlapping crises the world has experienced in 2020 make clear that resources from multiple sectors — government, private sector, and philanthropy — need to be deployed at multiple scales to better address societal challenges. In particular, science philanthropy has stepped up, helping to advance COVID-19 vaccine development, identify solutions to climate change, and make the tools of scientific inquiry more widely available.

As I write in my recently published book, Philanthropy and the Future of Science and Technology (Routledge, 2020), this linkage between science philanthropy and societal responsibility is one that needs to be continually strengthened and advanced as global challenges become more intertwined and as the relationship between science and society becomes more complex. In fact, science philanthropies have an important, yet often overlooked, role in raising the profile of the societal responsibility of research. One way to better understand the role science philanthropies can and should play in society is to draw on the responsible research and innovation (RRI) framework, a concept developed by scholars from fields such as science & technology policy and science & technology studies. Depending on its configuration, the RRI framework has roughly three core dimensions: anticipatory research that is forward-looking and in search of new discoveries, deliberative and inclusive approaches that better engage and integrate members of the public with the research process, and the adoption of reflexive and responsive dispositions by funders (along with those conducting research) to ensure that societal and public values are accounted for and integrated at the outset of a research effort.

Philanthropies that fund research can more explicitly consider this perspective — even just a little bit — when making their funding decisions, thereby helping to better infuse whatever support they provide for individuals, institutions, and networks with attention to broader societal concerns. For instance, doing so not only highlights the need for science philanthropies to identify and support high-quality early career researchers who are pursuing new avenues of science and technology research, but it also raises considerations of diversity, equity, and inclusion as equally important decision-making criteria for funding. The RRI framework also suggests that foundations working in science and technology should not only help to bring together networks of individual scholars and their host institutions, but that the horizon of such collaborations should be actively extended to include practitioners, decision-makers, users, and communities affected by such investigations. Philanthropies can take a further step and reflexively apply these perspectives to how they operate, how they set their strategies and grantmaking priorities, or even in how they directly manage scientific research infrastructure, which some philanthropic institutions have even begun to do within their own institutions….(More)”.

Why Doubt Is Essential to Science

Curated on Posted on by Stefaan Verhulst

Liv Grjebine at Scientific American: “The confidence people place in science is frequently based not on what it really is, but on what people would like it to be. When I asked students at the beginning of the year how they would define science, many of them replied that it is an objective way of discovering certainties about the world. But science cannot provide certainties. For example, a majority of Americans trust science as long as it does not challenge their existing beliefs. To the question “When science disagrees with the teachings of your religion, which one do you believe?,” 58 percent of North Americans favor religion; 33 percent science; and 6 percent say “it depends.”

But doubt in science is a feature, not a bug. Indeed, the paradox is that science, when properly functioning, questions accepted facts and yields both new knowledge and new questions—not certainty. Doubt does not create trust, nor does it help public understanding. So why should people trust a process that seems to require a troublesome state of uncertainty without always providing solid solutions?


As a historian of science, I would argue that it’s the responsibility of scientists and historians of science to show that the real power of science lies precisely in what is often perceived as its weakness: its drive to question and challenge a hypothesis. Indeed, the scientific approach requires changing our understanding of the natural world whenever new evidence emerges from either experimentation or observation. Scientific findings are hypotheses that encompass the state of knowledge at a given moment. In the long run, many of are challenged and even overturned. Doubt might be troubling, but it impels us towards a better understanding; certainties, as reassuring as they may seem, in fact undermine the scientific process….(More)”.