La crise de la COVID-19 et les finances publiques dans un contexte canadien. Revue ECU/EURO, N°55 (2020)
Bibliographic record
Abstract
Devant l’ampleur de la crise de la COVID-19, les gouvernements canadien et québécois ne sont pas restés les bras croisés. Presque chaque jour ou semaine, de la mi-mars à la fin juin 2020, ils ont annoncé des mesures de soutien ayant des impacts économiques importants. L’analyse s’intéresse aux réponses des administrations publiques canadienne et québécoise. Elle présente en premier lieu la réaction initiale, soit les annonces économiques gouvernementales visant à accroître les liquidités des individus et des entreprises, comme les reports de paiements d’impôts et taxes et les bonifications de programmes existants. Ensuite, l’analyse aborde l’élaboration et l’évolution de deux importants nouveaux programmes d’urgence mis en place par le gouvernement fédéral: la prestation canadienne d’urgence et la subvention salariale d’urgence du Canada. Pour terminer, l’analyse porte son regard sur les perspectives économiques et de finances publiques, notamment l’ampleur des déficits et les répercussions sur l’endettement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".