Re-thinking the coronavirus pandemic as a policy punctuation: COVID-19 as a path-clearing policy accelerator
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.015 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it