Snowing and Towing in Montréal: The Inspector General's Fight against Collusion in Two Industries
Bibliographic record
Abstract
Offices of Inspectors General (OIGs) provide a highly valuable service by fostering and promoting integrity, transparency, and accountability in government. These offices provide independent oversight and monitor governmental operations, acting as watchdogs for the people and helping to maintain or restore the public’s confidence in their institutions.\nThe mandate of Montréal’s OIG is to conduct administrative investigations and to oversee contracting processes and the implementation of contracts by the City of Montréal in order to prevent breaches of integrity and violations of rules. Created in 2014, Montréal’s OIG has already had a meaningful impact on public procurement and management policies. In this publication, we describe the OIG’s impact on two regulated industries: snow removal and towing, demonstrating in the process that an OIG can be critically important in the fight against collusion.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".