The Moral of the Story: Contesting Narratives at the Nexus of Science and Policy During COVID-19
Bibliographic record
Abstract
Abstract Using the case of the Scientific Advisory Group for Emergencies in the United Kingdom as illustration, this essay offers a framework for understanding the role of narratives and competition among narratives in mediating the relationships between scientific advisers and policymakers during the COVID-19 pandemic. Throughout the pandemic, competing judgments about scientific independence and democratic accountability, about the risks of action and inaction, and about the appropriate balance of costs and benefits to society as a whole and to subgroups of the population were filtered through the narrative perspectives of different discourse coalitions. This narrativization of the process had both positive and negative effects. On the one hand, it provided common platforms for the integration of disparate types of knowledge relevant to policymaking. On the other hand, narratives provided platforms for rival coalitions in ongoing contests that left unresolved the central normative questions of distributional fairness and democratic accountability.
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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 | 0.034 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.030 | 0.088 |
| Scholarly communication | 0.023 | 0.028 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".