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Record W2909858936

Je pars en Inde

2017· book· fr· W2909858936 on OpenAlexaboutno aff
Véronique Daudelin

Bibliographic record

VenueÉditions du Septentrion eBooks · 2017
Typebook
Languagefr
FieldSocial Sciences
TopicDiverse Cultural and Historical Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesArt
DOInot available

Abstract

fetched live from OpenAlex

A 29 ans, ecoeuree de sa vie en general et meme d'elle-meme, Veronique Daudelin part en Inde pour quatre mois en esperant que ce voyage changera sa vie. Ce ne sera pas aussi simple. Au coeur d'une crise existentielle assumee, la narratrice cherche «tout»: qui elle est, sa place dans le monde et un sens a son existence. Rien de moins. Elle cherche des indices non seulement a travers le yoga et la meditation, mais aussi a travers les «personnages» qu'elle croise en route: des refugies tibetains, un itinerant, un mort, un chien? De la traversee de l'Himalaya a un mariage indien en passant par un rituel chamanique, la voyageuse pose sur elle-meme un regard toujours lucide. Son recit, honnete et authentique, est a la fois profond et rempli d'humour. L'Inde n'y est en rien idealisee. La quete spirituelle non plus. Veronique Daudelin est diplomee du Conservatoire d'art dramatique de Quebec (2003). Elle a travaille comme comedienne pour diverses productions theâtrales, pour la tele et pour le Cirque du Soleil. En 2016, elle a egalement coanime l'emission Azimut, diffusee sur Evasion. Elle habite a Montreal.

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)0.001
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 score0.152
Threshold uncertainty score0.508

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0130.002
Scholarly communication0.0050.003
Open science0.0010.004
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.1520.052

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 head0.046
GPT teacher head0.269
Teacher spread0.223 · 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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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