Recherche littéraire / Literary Research
Bibliographic record
Abstract
As the annual peer-reviewed publication of the International Comparative Literature Association (ICLA), Recherche littéraire / Literary Research is an Open Access journal published by Peter Lang. Its mission is to inform comparative literature scholars worldwide of recent contributions to the field. To that end, it publishes scholarly essays, review essays discussing recent research developments in particular sub-fields of the discipline, as well as reviews of books on comparative topics. Scholarly essays are submitted to a double-blind peer review. Submissions by early-career comparative literature scholars are strongly encouraged. En tant que publication annuelle de l’Association internationale de littérature comparée (AILC), Recherche littéraire / Literary Research est une revue expertisée par des pair·e·s et publiée par Peter Lang en libre accès voie dorée. Elle vise à faire connaître aux comparatistes du monde entier les développements récents de la discipline. Dans ce but, la revue publie des articles de recherche scientifique, des essais critiques dressant l’état des lieux d’un domaine particulier de la littérature comparée, ainsi que des comptes rendus de livres sur des sujets comparatistes. Les articles de recherche sont soumis à une évaluation par des pair·e·s en double anonyme. Des soumissions par de jeunes chercheuses et chercheurs en littérature comparée sont fortement encouragées.
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 | 0.029 | 0.089 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.033 | 0.018 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.147 | 0.134 |
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".