Questions in Literary Theory: Jean Bessière's Contribution to Comparatism
Bibliographic record
Abstract
The words in epigraph are an example of an essential characteristic of Jean Bessiere's way of thinking, that of giving a central role to theory and to a type of thought which, in its development and in its practice, links Philosophy to Literature. It is therefore in the perspective of examining certain theoretical concepts applied to comparatist praxis that I wish to present his contribution to the understanding of Latin American diversity, which is the source for comparatism as it is practiced in this region. In an article published in Litterature comparee. Theorie et pratique (1998), (edited by Andre Lorant and Jean Bessiere and based on the Acts of the International Colloquium held at the University of Paris XII Val de Marne in 1993), I had the chance to reflect upon the conceptual and methodological changes in Comparative Literature in the second half of the century which has just ended, and to underline the contribution of theoretical discourse to traditional textual analysis, which was examined with regards to the appropriations on which its development feeds, or with regards to the borrowings which shape it. I attempted on this occasion to highlight the convergence and the complementarity of literary theory and comparative studies, such as they are described in those works, published in 1989, which are fundamental with
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.031 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| 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".