Bibliographic record
Abstract
A framework is developed in which the formation of gangs --- the criminal market structure --- is endogenous. We examine the impact of crime deterrence in this framework. It is shown that for a given gang structure, an increase in deterrence reduces criminal output. However, under identifiable circumstances, an increase in deterrence can also lead to an increase in the number of competing criminal gangs and to an increase in total illegal output. Nous étudions un monde dans lequel la formation de gangs criminels est endogène, l'ensemble des gangs criminels constituant la structure de marché d'un bien illégal. Nous étudions l'impact de la dissuasion du crime dans un tel cadre. Pour une structure de marché donnée, accroître la dissuasion tend à réduire la production du bien illégal. Cependant, dans certains cas, accroître la dissuasion peut conduire a un accroissement du nombre de gangs criminels dans le marché et donc, à une plus grande concurrence. Il est alors possible que la production du bien illégal augmente.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".