The Effect of Differences in Treatment of \nthe Canada Emergency Response Benefit \nacross Provincial and Territorial Income \nAssistance Programs
Bibliographic record
Abstract
La Prestation canadienne d'urgence (PCU) est un programme temporaire de transferts monétaires destiné aux travailleurs dont les revenus ont diminué en raison de la pandémie de la COVID-19. Certains travailleurs bénéficiant de la PCU reçoivent également de l'aide provinciale ou territoriale au revenu. Les objectifs et les définitions de la PCU n'ayant pas été clairement établis, le traitement dont elle fait l'objet varie considérablement selon les programmes d'aide au revenu provinciaux et territoriaux. Les auteures se demandent en quoi ces différences de traitement de la PCU par les programmes provinciaux d'aide au revenu touchent les clients de l'aide au revenu, selon les administrations. Elles se penchent sur les arguments qui auraient dû militer en faveur de l'entière exclusion de la PCI dans le calcul des prestations d'aide au revenu. Abstract: The Canada Emergency Response Benefit (CERB) is a temporary cash transfer program for workers who have reduced earnings due to the COVID-19 pandemic. Some workers receiving the CERB also receive provincial or territorial income assistance. A lack of clear objectives and definitions related to the CERB has led to the CERB being treated very differently by provincial and territorial income assistance (IA) programs. We look at how these different treatments of CERB under provincial income assistance programs affect IA clients across jurisdictions. We consider arguments for why the CERB should have been fully exempted from IA benefits.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".