MétaCan
Menu
Back to cohort
Record W3114036247 · doi:10.33137/rr.v43i3.35305

“Des responses et rencontres”: Frank Speech and Self-Knowledge in Guillaume Bouchet’s Serées

2020· article· en· W3114036247 on OpenAlexvenueno aff
Luke O’Sullivan

Bibliographic record

VenueRenaissance and Reformation · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical and Literary Analyses
Canadian institutionsnot available
Fundersnot available
KeywordsConversationSubject (documents)FeelingContext (archaeology)Style (visual arts)FranchiseHumanitiesSpanish Civil WarArtArt historySociologyLiteratureHistoryLawPolitical sciencePhilosophyEpistemology

Abstract

fetched live from OpenAlex

Guillaume Bouchet’s Serées (1584, 1597, 1598) constitute an exercise in commonplacing framed as a collection of tales told around a Poitevin dining table. They engage in a form of quasi-philosophical thinking staged by and for an urban merchant community, the social world in which Bouchet operated. The second book opens with a discussion of frank speech. Writing amid civil war, Bouchet takes up this “chatouilleux” subject by turning to Plutarch, the classical authority on parrhesia (truth-telling). Recycling Plutarch, though, Bouchet does not ask how or when to speak frankly but instead examines responses to “franchise” both in the tales and from the storytellers themselves. Around Bouchet’s table, talk of frank speech leads to awkward silences and conversation grinding to a halt. This serée illuminates a context for parrhesia distinct from the familiar arena of nobles counselling autocrats or performing “liberté.” Here, philosophical self-knowledge slips uncomfortably into a feeling of social self-consciousness, revealing a distinct conception of the ethics and epistemologies surrounding frankness.

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)0.007
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.015
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0150.043
Scholarly communication0.0110.007
Open science0.0010.003
Research integrity0.0060.007
Insufficient payload (model declined to judge)0.0020.000

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 head0.034
GPT teacher head0.252
Teacher spread0.218 · 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 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

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueRenaissance and ReformationSame topicHistorical and Literary AnalysesFrench-language works237,207