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Record W2018397186 · doi:10.1093/fs/knl101

Littératures mineures en langue majeure: Québec/Wallonie-Bruxelles.

2006· article· fr· W2018397186 on OpenAlexaboutno aff
Patrick Corcoran

Bibliographic record

VenueFrench Studies · 2006
Typearticle
Languagefr
FieldSocial Sciences
TopicFrench Language Learning Methods
Canadian institutionsnot available
Fundersnot available
KeywordsForce majeureArtArt historyPolitical science

Abstract

fetched live from OpenAlex

Textes réunis par Jean-Pierre Bertrand et Lise Gauvin. (Documents pour l'histoire des francophonies/Théorie, 1) Brussels, P.I.E. — Oxford, Peter Lang — Montreal, Presses de l'Université de Montréal, 2003. 320 pp. Pb £19.00. The concept of ‘littérature mineure’ elaborated by Deleuze and Guattari in their 1975 text on Kafka has proved a far less productive notion for the theorization of instances of postcoloniality than might have been expected, even with the gift of hindsight, given the omnipresence of the ‘rhizomatic’ and the ‘nomadic’ as key conceptual tools of postcolonial analysis ever since the publication of Mille Plateaux five years later. In the earlier text, Deleuze–Guattari remind readers that a ‘littérature mineure n'est pas celle d'une langue mineure, plutôt celle qu'une minorité fait dans une langue majeure’ (p. 29) and go on to identify three characteristics of such literature: ‘la déterritorialisation de la langue, le branchement de l'individuel sur l'immédiat-politique, l'agencement collectif d'énonciation’ (p. 33). The conception of ‘minor literature’ that emerges from the case study of Kafka is not one of any simple hierarchical configuration expressed as a binary (major/minor), but rather as a transversal revolutionary principle inhabiting the practice of literature and the interplay of plurilingualism within specific socio-political contexts.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.131
Threshold uncertainty score0.439

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0050.013
Science and technology studies0.0040.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.1310.011

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 head0.023
GPT teacher head0.327
Teacher spread0.303 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations0
Published2006
Admission routes1
Has abstractyes

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