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Record W580436788

Questions in Literary Theory: Jean Bessière's Contribution to Comparatism

2011· article· en· W580436788 on OpenAlexvenueno aff
Tânia Franco Carvalhal

Bibliographic record

VenueCanadian review of comparative literature · 2011
Typearticle
Languageen
FieldArts and Humanities
TopicLinguistics and Discourse Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsEpistemologyComplementarity (molecular biology)PraxisSociologyEpigraphPhilosophyLiterary theoryLiterary criticismLiteratureLinguistics
DOInot available

Abstract

fetched live from OpenAlex

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

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.966
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.044
GPT teacher head0.300
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2011
Admission routes1
Has abstractyes

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