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Record W2801413986 · doi:10.4000/contextes.6445

Du lecteur automate aux émotions universelles

2018· article· fr· W2801413986 on OpenAlexaff
Guillaume Pinson

Bibliographic record

VenueContextes · 2018
Typearticle
Languagefr
FieldArts and Humanities
TopicLiterature and Culture Studies
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsHumanitiesArtPhilosophy

Abstract

fetched live from OpenAlex

Cet article propose quelques pistes de réflexion sur une histoire de « la vie affective d’autrefois » (Lucien Febvre), en s’intéressant aux émotions dans l’histoire littéraire et culturelle de la presse du XIXe siècle. Comment le lecteur exprimait-il des sensibilités, certaines anxiétés, des émotions et des désirs, étroitement liés à la consommation des objets médiatiques ? Après des propositions synthétiques et méthodologiques, l’article s’intéresse aux appropriations intimes et émotionnelles du journal dans une série de sources d’époques, notamment romanesques. Du lecteur immergé dans sa lecture jusqu’aux grandes « émotions universelles » du journal de masse, on explore certaines de ces réactions corporelles, émotionnelles et sociales. La vie émotionnelle en contexte médiatique réunit « l’intérieur » (le corps affecté du lecteur) et « l’extérieur » (le corps social ému), ce qui nous mène à proposer que la « civilisation du journal » a été le cadre d’un remodelage profond des sensibilités à partir du XIXe siècle, et que les émotions ont joué un rôle important dans l’histoire de la communication.

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)0.006
metaresearch head score (Gemma)0.024
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.028
Threshold uncertainty score0.092

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.024
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0030.007
Scholarly communication0.0090.009
Open science0.0010.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0280.010

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 head0.020
GPT teacher head0.235
Teacher spread0.216 · 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
Published2018
Admission routes1
Has abstractyes

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