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The COVID-19 pandemic has been a data-political spectacle. Data are omnipresent in prediction and surveillance, and even in resistance to governmental measures. How have citizens, whose lives were suddenly governed by pandemic data, understood and reacted to the pandemic as a data-political phenomenon? Based on a study carried out in Denmark, we show how society became divided into those viewing themselves as supporters of the governmental approach to the COVID-19 pandemic, and those who oppose it. These groups seem to subscribe to very different truths. We argue, however, that both sides share a positivist ideal and think that data and facts ought to rule. Both sides have also come to acknowledge that data are not unambiguous, and both cast increasing doubts on political uses of data. Though the people agreeing with, and the people opposing, the government strategy are in many ways surprisingly similar with respect to epistemic norms, they differ in what they perceive as dangerous or desirable, and in who they believe are telling the “truth” about the pandemic. These different perceptions result in different types of pandemic-related activism. Resistance against restrictions is often understood as inspired by conspiracy theories and in some countries anti-restrictions activism has turned violent. In our case, however, we suggest that when looking at similarities and differences across both groups, the gap between those opposing and those agreeing with the government approach is not as unbridgeable as might be suggested by their beliefs in differing truths and the emerging societal division.
The COVID-19 pandemic has been a data-political spectacle. Data are omnipresent in prediction and surveillance, and even in resistance to governmental measures. How have citizens, whose lives were suddenly governed by pandemic data, understood and reacted to the pandemic as a data-political phenomenon? Based on a study carried out in Denmark, we show how society became divided into those viewing themselves as supporters of the governmental approach to the COVID-19 pandemic, and those who oppose it. These groups seem to subscribe to very different truths. We argue, however, that both sides share a positivist ideal and think that data and facts ought to rule. Both sides have also come to acknowledge that data are not unambiguous, and both cast increasing doubts on political uses of data. Though the people agreeing with, and the people opposing, the government strategy are in many ways surprisingly similar with respect to epistemic norms, they differ in what they perceive as dangerous or desirable, and in who they believe are telling the “truth” about the pandemic. These different perceptions result in different types of pandemic-related activism. Resistance against restrictions is often understood as inspired by conspiracy theories and in some countries anti-restrictions activism has turned violent. In our case, however, we suggest that when looking at similarities and differences across both groups, the gap between those opposing and those agreeing with the government approach is not as unbridgeable as might be suggested by their beliefs in differing truths and the emerging societal division.
This study explores how researchers’ analytical choices affect the reliability of scientific findings. Most discussions of reliability problems in science focus on systematic biases. We broaden the lens to emphasize the idiosyncrasy of conscious and unconscious decisions that researchers make during data analysis. We coordinated 161 researchers in 73 research teams and observed their research decisions as they used the same data to independently test the same prominent social science hypothesis: that greater immigration reduces support for social policies among the public. In this typical case of social science research, research teams reported both widely diverging numerical findings and substantive conclusions despite identical start conditions. Researchers’ expertise, prior beliefs, and expectations barely predict the wide variation in research outcomes. More than 95% of the total variance in numerical results remains unexplained even after qualitative coding of all identifiable decisions in each team’s workflow. This reveals a universe of uncertainty that remains hidden when considering a single study in isolation. The idiosyncratic nature of how researchers’ results and conclusions varied is a previously underappreciated explanation for why many scientific hypotheses remain contested. These results call for greater epistemic humility and clarity in reporting scientific findings.
This review begins with a detailed focus on the Turnaway Study, which addresses associations among early abortion, later abortion, and denied abortion relative to various outcomes including mental health indicators. The Turnaway Study was comprised of 516 women; however, an exact percentage of the population is not discernable due to missing information. Extrapolating from what is known reveals a likely low of 0.32% to a maximum of 3.18% of participants sampled from the available the pool. Motivation for conducting the Turnaway Study, methodological deficiencies (sampling issues and others), and bias are specifically addressed. Despite serious departures from accepted scientific practices, journals in psychology and medicine have published dozens of articles generated from the study’s data. The high volume of one-sided publications has stifled dialogue on potential adverse psychological consequences of this common procedure. Following a critical analysis of the Turnaway Study, an overview of the strongest studies on abortion and mental health is offered. This comprehensive literature comprised of numerous large-scale studies from across the globe has been largely overlooked by scientists and the public, while the Turnaway Study dominates the media, information provided to women, and legal challenges involving abortion restrictions. In the final section of this article, literature reviews by professional organizations are considered, demonstrating that the biased science characterizing the Turnaway Study is aligned with a pervasive and systemic phenomenon wherein deriving reliable and valid results via careful attention to methodology and scrutiny by the scientific community have been supplanted by politics.
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