Bibliographic record
Abstract
From the Editor's Desk Carine Bourget, Rédactrice en chef Je remercie vivement Joyce Beckwith, Elizabeth Knutson et Jennifer Perlmutter pour leur service en tant que membres du comité de la rédaction. Je souhaite la bienvenue à Sandra Descourtis (Emory), Rebecca Fortgang (Fall Mountain Regional High School) et Micah True (University of Alberta) qui prennent la relève. Carine Bourget, Rédactrice en chef [End Page 11] [End Page 12] Announcements The French Review welcomes articles for a Special Issue on: Annie Ernaux This Special Issue, to be published in May 2025 (Vol. 98.4), will be devoted to Annie Ernaux, the first French woman to win the Nobel Prize for Literature. Successful submissions will make original contributions to the scholarly literature on any aspect of Ernaux's oeuvre and its reception, or offer pedagogical strategies and resources for teaching her works. Deadline for submission: May 15, 2024. The French Review welcomes articles for a Special Section on: The Nobel Prize (in Literature) and France/French This Special section, to be published in May 2025 (Vol. 98.4) or October 2025 (Vol. 99.1), will be devoted to the place of France/French in the history of the Nobel Prize (in Literature). Successful submissions will make original contributions to the scholarly literature on the literary and cultural aspects of the place of France and the French language in the attribution of the prize (or not), classifications, reception, etc. Deadline for submission: May 15, 2024. Authors should consult our Guide for Authors for the formatting requirements. Submissions should be sent to the Editor in Chief (editorfr@frenchteachers.org). [End Page 13] Copyright © 2024 American Association of Teachers of French
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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 | 0.006 | 0.066 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.287 | 0.278 |
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".