Re-thinking the coronavirus pandemic as a policy punctuation: COVID-19 as a path-clearing policy accelerator
Bibliographic record
Abstract
Abstract This article joins with others in this special issue to examine the evolution of our understanding of how the coronavirus disease (COVID)-19 pandemic impacted policy ideas and routines across a wide variety of sectors of government activity. Did policy ideas and routines transform as a result of the pandemic or were they merely a continuation of the status quo ante? If they did transform, are the transformations temporary in nature or likely to lead to significant, deep and permanent reform to existing policy paths and trajectories? As this article sets out, the literature on policy punctuations has evolved and helps us understand the impact of COVID-19 on policy-making but tends to conflate several distinct aspects of path trajectories and deviations under the general concept of “critical junctures” which muddy reflections and findings. Once the different possible types of punctuations have been clarified, however, the result is a set of concepts related to path creation and disruption—especially that of “path clearing”—which are better able to provide an explanation of the kinds of policy change to be expected to result from the impact of events such as the 2019 coronavirus pandemic.
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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 | 0.017 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.046 |
| Scholarly communication | 0.018 | 0.018 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".