Are franchisees more prone to employment standards violations than other businesses? Evidence from Ontario, Canada
Bibliographic record
Abstract
Using an administrative dataset from the Ontario Ministry of Labour, we investigate three hypotheses about employment standards violations among franchised businesses: (1) franchisees have a higher probability of violating employment standards than other businesses, (2) franchisees have a higher probability of monetary/wage-related ES violations than other businesses, and (3) franchisees have a lower probability of repaying monetary/wage-related violations than other businesses. The results of our statistical models suggest that overall, franchisees are indeed more likely to violate ES, have a higher probability of monetary/wage-related violations, and are less likely to repay such violations. However, the results vary substantially by industry. While franchisees had only marginally higher probabilities of an ES violation in two of the seven industry-groups examined, five of the seven industries showed substantially higher probabilities of a monetary violation. The results also show that franchisees in three industry groups (retail, accommodation and food services, and education, public administration, healthcare and social services) are particularly prone to monetary violations. JEL codes: J83, J88, J89
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".