Governments and parliaments in a state of emergency: what can we learn from the COVID-19 pandemic?
Bibliographic record
Abstract
What happens in a state of emergency that is prolonged and unrelated to security with respect to the powers afforded to or used by the executive, checks and balances, and cooperation between the government, parliament, and sub-national authorities? This article investigates the variation in ‘executive aggrandisement’ (a temporary reduction in influence and oversight capacity of formal institutions vis-à-vis the executive) during the COVID-19 pandemic in six parliamentary democracies. We theorise that this variation can be in part explained based on path dependence. We explore how pre-pandemic levels of executive dominance and policy centralisation affect executive aggrandisement during the 2020–2022 emergency across our sample of countries. We show that Canada and Germany experienced little to no aggrandisement. In France, Israel, Italy, and the United Kingdom, government rule increased throughout the crisis at the expense of parliament and sub-national authorities. In line with our expectations, we find that most facets of the process of executive aggrandisement in a state of emergency can be interpreted in view of prior institutional arrangements. The outlier elements can be explained by considering circumstantial factors. Our evidence contributes to the literature on the political consequences of COVID-19 by filling some gaps regarding the roots of executive aggrandisement.
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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 | 0.004 | 0.017 |
| 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 | 0.006 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".