Abrams, Barbara. Forensic Storytelling and the Literary Roots of Early Modern Feminism, ReSisters. New York, London: Routledge, 2024.
Bibliographic record
Abstract
This important archival study opens up the rich possibilities of what Abrams' calls "forensic storytelling."This strategy of scholarly engagement is the author's new way into the story of early modern women's writing as a literature of resistance in 18 th century France.Or, in Abrams' terms, a legacy of French "reSisters."In this study Abrams focuses on the story of the incarceration of three French women in the 18 th century.The stories are themselves forensic as is the forensic data she uses to make her case about these women's lives and their relationship to the literature of early modern feminism.Abrams interrogates the factums, the letters, notes, and arguments that these women put together to plead their own cases at the time.Scouring the archives at the Bibliotèque de l'Arsenal, Abrams brings us into the worlds of three of these women.Investigating their words, and their evidentiary files, she tells their stories, but she also places them in a broader context, both historical and literary.As I read this book, Barbara Abrams's meticulous archival research constitutes in literary and historical terms, forensic expertise that resonates with the work of the American Academy of Forensic Science (AAFS); their work relates to any science used for the purpose of the law.Moreover, as they explain, the forensic scientist is often called upon as an expert witness in court but that this expert witness, "as opposed to the ordinary or 'fact' witness, is someone who is "permitted to testify not just about what the results of testing or analysis were ('facts'), but also to give opinion about what those results might mean." 1 She testifies to not just what is in the archive but is that scholarly expert who is uniquely positioned to tell us what this evidence means.This is precisely what she does in her aptly titled, Forensic Storytelling.She digs out and helps illuminatean extraordinary cache of French 18th century women's writing, a whole genre that has been overlooked as such, and, to date, only partially studied.On this score, Abrams is both gracious and eloquent in the ways she both appreciates and builds on prior scholarship.This is not about conquest as much as it is about a broadening, an opening of this work to new questions and novel perspectives that build from prior scholarly inquiry.She enhances and expands those studies to
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.006 | 0.018 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 0.009 |
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".