Le programme de Subvention salariale d’urgence du Canada et la croissance et le taux de survie des entreprises pendant la pandémie de COVID-19 au Canada
Bibliographic record
Abstract
La pandémie de COVID-19 a eu une incidence majeure sur les entreprises en 2020. En raison de celle-ci, le gouvernement du Canada a instauré des mesures pour soutenir les personnes et les entreprises pendant la pandémie. Le plus important programme destiné aux entreprises a été le programme de Subvention salariale d’urgence du Canada (SSUC). Cet article présente des données à l’échelle des entreprises portant sur le lien entre l’utilisation des programmes de la SSUC et la survie et la croissance des entreprises, en tenant compte des caractéristiques prépandémiques des entreprises et, lorsque cela était possible, de leur utilisation de deux autres importants programmes, à savoir le programme du Compte d’urgence pour les entreprises canadiennes et le programme d’Aide d’urgence du Canada pour le loyer commercial.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".