La prévention des risques éthiques dans les grands projets d’infrastructure : résultats d’une étude
Bibliographic record
Abstract
Les grands projets d’infrastructure (GPI) requièrent des investissements publics considérables. Il est bien documenté qu’ils font l’objet de transgressions de toutes sortes, à commencer par la corruption, mais aussi la collusion, le favoritisme et autres transgressions apparentées. Cet article présente les résultats d’une recherche conduite au Québec qui vise à proposer des mesures de prévention et d’atténuation des risques éthiques dans les GPI. Le contexte de la recherche (problématique et cadre d’analyse) est d’abord exposé. Sont ensuite présentés les principaux facteurs de risque éthique déterminés par nos travaux. La dernière section s’attarde aux quatre volets dans lesquels sont formulées des stratégies de prévention et d’atténuation des risques éthiques.
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 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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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; both teacher heads agree on what is shown here.
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".