Prisons on the Edge: Perspectives on the Late Medieval and Early Modern History of Confinement in the Francosphere
Bibliographic record
Abstract
Abstract It is high time we took stock of the field of carceral history—something particularly true for the French-speaking world. In the last twenty years, the Francosphere has seen a frenzy of research activity centred on the prison, and more broadly around multiple forms of confinement. The field is being radically transformed under the watchful eye of a whole new generation of researchers who have produced numerous monographs, articles and colloquia on the subject. The research and thinking about the prison model that the current generation is developing diverges so completely from the classical revisionist historiography as to be unrecognisable, transfigured from previous iterations. The purpose of this article is to explore those recent transformations of the field following three parameters. The first is linguistic (reviewing only the work emerging from the Francosphere); the second is one of academic discipline (History); and the third is temporal (the emphasis is on research touching on the long pre-Revolution period). These three parameters provide a focused view, while remaining sufficiently widely relevant to identify the dominant trends in French-language research. This is set in the context of past research, of work that has been done elsewhere, and of what sets Francophone research apart from corresponding English-language scholarship.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".