Quelques considérations sur le sentiment et la condition d’isolement des victimes de la peur du crime
Bibliographic record
Abstract
Cet article traite d’un phénomène peu exploité d’un point de vue sociologique : l’auto-exclusion des personnes victimes d’actes criminels. L’auteure propose une réflexion sur l’expérience d’une victimisation criminelle et de ses conséquences non seulement physiques ou économiques, mais aussi et surtout en termes psychologiques et émotionnels. Dans une telle situation, la personne développera des stratégies d’isolement, voire de rupture avec un environnement qui lui était familier, conséquence de la peur produite et intériorisée. Cette situation se prolongera durant de longs moments pouvant aller jusqu’à quelques années; pour certaines personnes, cette peur et cette insécurité ne disparaîtront pas, elles devront apprendre à vivre avec celles-ci.
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 | 0.036 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.007 | 0.007 |
| 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".