The Economic and Long-Term\nHealth Consequences of Canadian\nCOVID-19 Lockdowns
Bibliographic record
Abstract
Pour enrayer la propagation exponentielle de la COVID-19, maints gouvernements ont restreint l'activité économique en procédant à des confinements d'activité. Nous modélisons ces restrictions comme des chocs subis par la productivité dans différents secteurs d'activité et en suivons les répercussions sur l'équilibre économique global, grâce à des techniques inspirées de l'économie des réseaux de production. Nous associons ce modèle économique à un modèle épidémiologique d'incidence des chocs de revenu sur la santé à long terme. Tant sur le plan de la santé à long terme que sur le plan économique, il est plus avantageux de maintenir en activité les secteurs en amont, comme le transport, la fabrication et le commerce de gros, que les secteurs de la consommation directe, comme le commerce de détail et la restauration. Abstract: To prevent exponential spread of COVID-19, many governments restricted economic activity through lockdowns. We model these restrictions as shocks to productivity by sector and trace total equilibrium effects across the economy using techniques from production network economics. We combine this economic model with an epidemiological model of income shocks to long-term health. On both long-run health and economic grounds, it is better to keep upstream sectors such as transportation, manufacturing, and wholesale open than consumer-facing sectors such as retail and restaurants.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".