The fragility of truth: Social epistemology in a time of polarization and pandemic
Bibliographic record
Abstract
presenting selected papers from the 2022 McGill Advanced Study Institute in Cultural Psychiatry on "The Fragility of Truth: Social Epistemology in a Time of Polarization and Pandemic." The COVID-19 pandemic, political polarization, and the climate crisis have revealed that large segments of the population do not trust the best available knowledge and expertise in making vital decisions regarding their health, the governance of society, and the fate of the planet. What guides information-seeking, trust in authority, and decision-making in each of these domains? Articles in this issue include case studies of the dynamics of misinformation and disinformation; the adaptive functions and pathologies of belief, paranoia, and conspiracy theories; and strategies to foster and maintain diverse knowledge ecologies. Efforts to understand the psychological dynamics of pathological conviction have something useful to teach us about our vulnerability as knowers and believers. However, this individual psychological account needs to be supplemented with a broader social view of the politics of knowledge and epistemic authority that can inform efforts to create healthy information ecologies and strengthen the civic institutions and practices needed to provide well-informed pictures of the world as a basis for deliberative democracy, pluralism, and co-existence.
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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 | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.022 | 0.030 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".