Punctuating the equilibrium: an application of policy theory to COVID-19
Bibliographic record
Abstract
COVID-19 has taught us that, when inadequately addressed, preexisting policy problems (e.g. weak coordination of healthcare and gaps in income supports) exacerbate the cost of crises (including deaths) and make policy responses more difficult. On a more hopeful note, the pandemic has also revealed that policymakers and bureaucrats, reputed as defenders of the status quo and glacially paced, are capable of moving nimbly when seized with necessity. This manuscript draws on Baumgartner and Jones’ punctuated-equilibrium theory to analyze and demonstrate how policy responses to the pandemic, largely in Canada but also globally, were shaped by preexisting problems (periods of equilibrium). It then raises the question: Will future policy reflect lessons learned through COVID-19, to not only mitigate risks from further crises, but also tackle many other policy challenges? It would seem we can no longer accept the excuse that problems are too complex or time-consuming to tackle.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".