The Impact of Regionally Differentiated Entitlement to EI on Charter-Protected Canadians
Bibliographic record
Abstract
Under Canada’s Employment Insurance (EI) program, access to unemployment benefits varies according to the regional unemployment rate. Previous studies have shown that this regime works to the disadvantage of certain provinces and urban areas. In this paper we measure the impact of the variable regional entrance requirements on specific minority workers, including visible minorities, linguistic minorities, recent immigrants, and naturalized citizens. We find that over the period 2000-2010, the regional variation in access to EI results in certain minority workers being required to work modestly more hours to qualify for EI than the average worker. Though the findings with regard to minority workers are modest, the differential treatment of workers by region remains problematic as a matter of both fairness and policy design. Because there are political barriers blocking the elimination or modification of the regional entrance requirements in Parliament, it may be fruitful to turn to the courts in pursuing reform. We provide a legal analysis under s. 15 of the Canadian Charter of Rights and Freedoms to investigate whether the impact of the regional entrance requirements could be considered unconstitutional adverse effects discrimination. We conclude that while the data does not support a constitutional claim today, if the differential, negative impact on minority workers increases in the future the chances of an equality rights challenge succeeding would also grow.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".