Bibliographic record
Abstract
Dans cet article, on endogénéise la structure horizontale du marché des drogues illicites. L'hypothèse principale du modèle est que la probabilité de détection d'une organisation criminelle dépend de sa part de marché. On met en évidence le fait qu'une politique répressive plus sévère encourage les entrepreneurs criminels en place à laisser pénétrer d'autres organisations sur le marché de la drogue. D'un côté, la répression diminue les quantités produites par chaque organisation criminelle, mais, d'un autre côté, elle incite la création de nouvelles entreprises, ce qui annule, au niveau agrégé, l'eet de la réduction de la production individuelle. On montre notamment que la quantité et le prix d'équilibre sont indépendants du niveau de répression, alors que l'objectif des politiques publiques de lutte contre la drogue est d'en réduire la consommation. Ainsi, l'augmentation des fonds alloués à la répression s'avère inecace.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".