Long-term policy impacts of the coronavirus: normalization, adaptation, and acceleration in the post-COVID state
Bibliographic record
Abstract
Abstract This paper offers an analysis of the theoretical and empirical challenges the coronavirus pandemic poses for theories of policy change. Critical events like coronavirus disease are potentially powerful destabilizers that can trigger discontinuity in policy trajectories and thus are an opportunity for accentuating path shifts. In this paper, we argue that three dynamic pathways of change are possible and must be considered when analysing post-COVID policymaking: normalization, adaptation, and acceleration. These different pathways need to be explored in order to understand the mid- and long-term policy effects of the pandemic. This introduction contextualizes the articles in this special issue, situating them broadly within two broad categories: (a) assessment of how the coronavirus disease pandemic should be understood as a crisis event, and its role in relationship to mechanisms of policy change; and (b) mapping the future contours of the pandemic’s impact on substantive policy areas, including education, health care, public finance, social protection, population ageing, the future of work, and violence against women.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".