L’analyse stratégique et quelques développements récents en criminologie
Bibliographic record
Abstract
Strategic analysis views crime as a confrontation and as a mean to an end. It is characterised by : 1) it concentrates on crime; 2) it takes cognizance of the circumstances under which the crime is committed; 3) it presents the crime as a decision influenced by its anticipated results. Felson's routine activity approach, which is similar to strategic analysis, is presented in this article. Other recent developments in criminology have made it possible to present several assertions with a view to explaining certain aspects of theft, in particular, the choice of target. These assertions are : 1) thefts vary according to the opportunities offered potential thieves; 2) opportunity is defined as the contact between a potential criminal and a suitable target; 3) the number of contacts between potential criminals and suitable targets varies directly with the number of targets and their accessibility; 4) the suitability of targets varies in direct proportion to their value and vulnerability. It varies in inverse proportion to their inertia.
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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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".