When public‐sector salaries become public knowledge: Academic salaries and Ontario's Public Sector Salary Disclosure Act
Bibliographic record
Abstract
Abstract:The effects of salary disclosure on public‐sector compensation have long been a source of controversy in political and academic circles. Some commentators suggest that because of political pressure and closer public scrutiny, salary disclosure is a good thing because it results in pay that is both lower than it would otherwise be and more sensitive to performance. On the other hand, disclosure raises serious privacy considerations and could also have an inflationary effect on salaries unless all elements in a causal chain linking public knowledge and lower pay are firmly in place. In this study, the authors examine the implications of Ontario's Public Sector Salary Disclosure Act with respect to university‐sector salaries. The main conclusions are that salary disclosure, in general, and in the academic sector in particular, has never fully accounted for proper comparability issues and has not been updated to reflect adjustments for inflation. The act also raises important questions of privacy that have not been fully addressed. Perhaps most notably, there is no evidence suggesting that salary disclosure has much of an influence in off‐setting other factors affecting salary growth.
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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 | 0.006 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".