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Record W3014038591

Comment j'ai gagné le Canada

2019· book· fr· W3014038591 on OpenAlexaboutno aff
Roland Bonvalet

Bibliographic record

VenueLe mot et le reste eBooks · 2019
Typebook
Languagefr
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesArtEthnologyHistory
DOInot available

Abstract

fetched live from OpenAlex

Dans cet ouvrage au style trepidant, l’auteur nous invite a la traversee du Canada de la fin des annees cinquante. Ici, fiction et realite se melangent dans des aventures de petits boulots, de rencontres et de grands espaces, de Montreal a Vancouver, avec 29 cents dans sa poche. Il y a aussi la route, l’auto-stop, l’histoire de la ruee vers l’or, les trains de nuit et les filles d’un soir ou d’une vie. C’est aussi l’histoire d’une langue : « mon metier, c’est la langue francaise », dit le narrateur. Conteur insatiable, ses propos « vocalisent avec tous les accents possibles et imaginables », dans un show epoustouflant. Roland Bonvalet est ne en 1925 dans le Perche, en France. Au debut des annees cinquante, il repond a l’appel des grands espaces et s’embarque pour le Canada. Apres une errance picaresque, il devient tres vite correspondant pour Radio Canada a Vancouver tout en poursuivant des etudes a l’Universite de Colombie britannique. Il passe ensuite quelques annees en Californie ou il se lie d’amitie avec Raymond Federman, puis il revient enseigner a l’universite en Alberta a Edmonton, ou il decede en 1980

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: Other · Consensus signal: Other
Teacher disagreement score0.063
Threshold uncertainty score0.228

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0270.005
Scholarly communication0.0110.003
Open science0.0010.002
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.0370.005

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 head0.018
GPT teacher head0.225
Teacher spread0.207 · 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
GenreOther

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
Published2019
Admission routes1
Has abstractyes

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Same venueLe mot et le reste eBooksSame topicCanadian Identity and HistoryFrench-language works237,207