Bibliographic record
Abstract
This work is a confluence of studies in historical, sociological, literary-critical and feminist perspectives and examines forensic storytelling in 18th-century France. As publications shift from the theatre to the courtroom, stories give individuals a collective sense of purpose. Masculine forms of written expression including: Les Lettres de Cachet, Les memoires judiciaires, Les Causes celebres, all provide a striking admission that women of all classes had very limited agency of expression. Current studies of French literature in the Eighteenth Century provide a paradoxical testimony to the makings of “First-Wave” feminism. While the rise of the novel provided a way for some educated women to express themselves through their writing, the then all male French Academy dictated and defined the parameters of French literary acceptability and tradition. Within these confines, women were by and large precluded from entering into the larger and more respected literary circles. Letters, memoires, lists and objects enclosed in legal briefs belonging to women who were consigned to the prison, convent or workhouses serve as the basis for forensic literature, helping to piece together their real stories. These authentic life stories have literary as well as historical merit.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".