Do COVID-19 Policies Affect Mobility\nBehaviour? Evidence from 75 Canadian\nand American Cities
Bibliographic record
Abstract
Les auteurs proposent une nouvelle mesure de la vigueur des politiques visant la COVID-19 dans 75 villes canadiennes et americaines et estiment les repercussions de ces politiques sur les profils de mobilite dans chaque ville. A l'aide d'un nouveau jeu de donnees sur cinq indicateurs relatifs aux politiques municipales visant la COVID-19 pour chacune des 75 villes, qu'ils associent a 11 indicateurs relatifs aux politiques provinciales ou etatiques, les auteurs elaborent une mesure quotidienne estimative de la « vigueur » de la combinaison de politiques provinciales ou etatiques et municipales visant la COVID-19 dans chaque ville. Ils estiment ensuite les repercussions de ces politiques sur le comportement de mobilite subsequent au moyen de modeles dynamiques de series chronologiques. Les resultats qu'obtiennent les auteurs corro-borent manifestement les repercussions des politiques sur le comportement de mobilite subsequent, mais les differences qu'ils observent entre les villes canadiennes et americaines sont globalement peu nombreus-es. Les auteurs traitent de la signification de ces resultats tant pour la recherche sur les politiques visant la COVID-19 que pour la recherche comparative sur d'autres politiques urbaines dans des environnements ou les decisions relatives aux politiques sont prises par plusieurs ordres de gouvernement. Abstract: We construct a new measure of the aggressiveness of COVID-19 policies in 75 Canadian and American cities and estimate the effect of these policies on mobility patterns in each city. Using a new dataset of five municipal COVID-19 policy indicators for each of our 75 cities, combined with 11 provincial/state policy indicators, we estimate a daily measure of the "aggressiveness" of the provincial/state and municipal COVID-19 policy mix in each city. We then estimate the effects of these policies on subsequent mobility behaviour using dynamic time series models. We find strong evidence of policy effects on subsequent mobility behaviour, but few overall differences between Canadian and American cities. We discuss the significance of our findings both for COVID-19 policy research and for other comparative urban policy research in multilevel policy environments.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".