Détecter et prévenir la collusion dans les marchés publics en construction: Meilleures pratiques favorisant la concurrence
Bibliographic record
Abstract
With assertion of collusive behavior in public construction projects, this report looks at the best practices meant to detect and to deter collusion in procurement. Based on regulatory frameworks, an overview of actual processes in public procurement and of construction industry regulation is explored. The economic analysis of tender as a bidding process, and of a cartel's internal logic, helps understand the impact of collusive behavior in public procurement. Also, drawing on best practices, this report suggests the means to detect and to deter collusion with improvements to the Québec's public procurement framework. Dans la foulée d'allégations de collusion dans les contrats publics en construction, le présent rapport s'intéresse aux meilleures pratiques visant à détecter et prévenir la collusion dans les marchés publics. Sur la base des cadres réglementaires spécifiques, un survol des pratiques actuelles dans les marchés publics et de l'organisation du secteur de la construction est proposé. L'analyse économique de l'appel d'offres comme forme d'enchère, et du fonctionnement d'un cartel, permet aussi de mieux comprendre l'impact de la collusion dans les marchés publics. Ce rapport propose finalement, à partir d'un ensemble de pratiques reconnues, les moyens de détecter et de prévenir la collusion et des pistes d'action à mettre en place dans le cadre des marchés publics québécois.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.062 | 0.172 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".