Bibliographic record
Abstract
Within the “culture wars” dividing many nations politically, there is another persistent division, intensified during the COVID-19 pandemic, over the trustworthiness of consensus science-the so-called “crisis of expertise.” Perhaps counterintuitively, however, the solution is likely not to wear “Because Science” T-shirts while insisting on “cold, hard facts.” Indeed, a certain level of modesty-regarding the uncertainties and tentativeness of even the best science-is necessary for the type of understanding and communication that might convince someone to change their scientific beliefs. While some scholars identify an anti-science ideology in certain segments of the citizenry, we should not ignore the ideological, almost religious, belief structures on both sides in the crisis of expertise. The purpose of this book is to analyze the crisis of expertise in terms of ideology, defined in this book as an inevitable worldview. Acknowledging the difficulty of useful discourse between groups who seem to live in different realities, the book employs a unique constellation of disciplinary frameworks to diagnose the crisis of expertise, including sociology of science, anthropology of religion, art history, and Wittgenstein's philosophy of language. Reconceiving science as a field of numerous uncertainties, together with recognizing each side in the crisis of expertise as having faith-like commitments, will best serve the goals of self-understanding and persuasive communication with respect to scientific disputes in the culture wars generally and specifically in governmental policy contexts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.906 | 0.877 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".