Executive decision‐making during the COVID‐19 emergency period
Bibliographic record
Abstract
Beginning in March of 2020, the unprecedented circumstances of the COVID-19 pandemic caused a shift in the ways in which governments, and all organizations, performed many of their functions, including the ways in which they make decisions. In Westminster parliamentary democracies, the executive branch-with the support of the public service-has the capacity to respond quickly and decisively to matters at hand, which can make the system particularly well suited to deal with emergencies. However, the expedited approach can come at some cost in the sense that a higher tolerance for risk earlier in the process can create an increased need for problem-solving later on. This article explores how the Canadian government approached decision-making during the COVID-19 period, specifically within the period between March and August of 2020. Decision-making processes were truncated and modified to meet the challenges of the time, and the federal public service was widely praised for its nimbleness and responsiveness.
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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 | 0.036 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.025 | 0.015 |
| Scholarly communication | 0.017 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".