Are franchisees more prone to employment standards violations than other businesses? Evidence from Ontario, Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it