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Record W4312907029 · doi:10.4000/babel.12855

Joseph Boyden. A Critical Interview

2021· article· en· W4312907029 on OpenAlexaboutno aff
Élisabeth Bouzonviller, Anne Garrait-Bourrier

Bibliographic record

VenueBabel · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsPublishingContext (archaeology)SociologyArt historyPromotion (chess)ArtClassicsMedia studiesHistoryLawPolitical scienceLiteraturePolitics

Abstract

fetched live from OpenAlex

This interview was conceived and ran by Anne Garrait-Bourrier, Professor in Cultural Studies at Clermont-Auvergne University and Elisabeth Bouzonviller, Professor of American Literature at Jean Monnet University. This article contains an original interview of the Canadian writer Joseph Boyden, recorded on December 12, 2014, at the University Blaise-Pascal, in Clermont-Ferrand. Joseph Boyden was on an 18 month-European tour for the promotion of his latest novel, The Orenda. The French part of this long tour was organized by his French editor, Albin Michel and more specifically by Francis Geffard, the publishing director of the Terre d’Amérique collection, who was the person who discovered Boyden and imposed him in France, as stipulated in Boyden’s very grateful acknowledgements at the end of the Penguin edition of his first novel. The questions asked mainly deal with Boyden’s The Orenda, hence the necessity to introduce the author and his work and replace this novel in a broader literary context.

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)0.005
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.506
Threshold uncertainty score0.982

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0240.006
Scholarly communication0.0040.004
Open science0.0010.003
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0240.004

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 head0.037
GPT teacher head0.307
Teacher spread0.270 · 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 designQualitative
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
Published2021
Admission routes1
Has abstractyes

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