Bibliographic record
Abstract
Considering debates frequently raised in France concerning the situation of pre-trial detention, the author identifies some trends: the constant reference to statistics; lack of efforts to precise the meaning of indicators used in making a demonstration — in fact the same statistics can be used to demonstrate contradictory theses —, a largely spread habit to always speak of more : more use of pre-trial incarceration, more pre-trial detainees... Those practices, argue the author, lead to ignore important changes in trends and to avoid questioning the meaning of those. The limited interest in research using more sophisticated indicators — that could add usefully informations to the data published regularly by the prison administrations —, international comparisons between data not necessarily comparable, references to old statistics, all result in everyone continuing to attribute to France the European championship in terms of pre-trial detention, while the actual situation could be totally different. Considering all those elements, the author presents new bases to reanimate the debate on the question of the use of pre-trial detention.
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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 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".