Quebec's Bill 1: A Case Study in Anti-Corruption Legislation and the Barriers to Evidence-Based Law-Making
Bibliographic record
Abstract
Corruption is a significant problem around the world. Large-scale public works projects are especially prone to corruption. Much international effort has been devoted to fighting corruption, but the impact of these efforts is debatable. Public-sector procurement in the Canadian province of Quebec has, since 2009, been beset by scandal. After defeat of the Liberal government in 2012, the first bill introduced by the new Parti Québécois government was an anti-corruption measure. The heart of Bill 1 is a system by which construction contractors have to demonstrate "integrity" in order to bid on public contracts. Quebec's lawmakers could have looked to international and national anti-corruption instruments, a vast literature, and practical examples from other jurisdictions. Instead, there is almost no reference in the debates to this anti-corruption context. The lawmaking process was driven by other imperatives, particularly speed. The author draws conclusions for anyone wishing to influence the lawmaking process.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.035 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.011 | 0.007 |
| 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".