The Impact of the Ontarian Minimum Wage on the Unemployment of Women and the Young in Ontario: A Note
Bibliographic record
Abstract
The purpose of this research note is to apply to Ontario a methodology developed and applied by one of the authors (Cousineau 1990) to measure the impact of Quebec's minimum wage on the unemployment of women and the young. This is of interest for two reasons. First, it allows us to examine the robustness of the methodology used by Cousineau (1990); and second, it permits us to contribute to the debate now ongoing in Ontario as to the impacts of raising the minimum wage in the province. This is of some interest, given the paucity of information on these impacts. The paper is divided into three parts. In the first one, we briefly summarize the analytical framework used. In the second, we discuss the data and variables used. In the third, we present and analyze régression results for Ontario, compare them to those obtained for Québec and use them to examine the proposed increase in the minimum wage.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".