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Record W2468841831

Le profil des bénévoles en loisir de 2001 et 2012 : changements ou similitudes?

2013· article· fr· W2468841831 on OpenAlexaboutno aff
David Leclerc

Bibliographic record

Venuenot available
Typearticle
Languagefr
FieldPsychology
TopicAdventure Sports and Sensation Seeking
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesArt
DOInot available

Abstract

fetched live from OpenAlex

En 10 ans, le profil des benevoles en loisir au Quebec a-t-il change ? Pour repondre a cette question, le Laboratoire en loisir et vie communautaire vient de proceder a une seconde enquete aupres des benevoles en loisir. Le present bulletin expose quelques donnees comparatives des etudes « Le benevolat en loisir » de 2001 et « Le benevolat en loisir, 10 ans apres » de 2012. Il fait etat des premiers resultats de l’enquete de 2012. Les lecteurs constateront que ce n’est pas tant les changements du profil des benevoles en loisir qui attirent l’attention mais plutot les similitudes. Les caracteristiques marquantes en 2001 le sont encore plus en 2012. A noter que ce bulletin prepare la sortie d’un autre bulletin qui sera diffuse au printemps 2013. A cette occasion, les principaux resultats de l’etude « Le benevolat en loisir, 10 ans apres » seront devoiles.

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)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.800
Threshold uncertainty score0.403

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0030.003
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.051
GPT teacher head0.348
Teacher spread0.296 · 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 designObservational
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
Published2013
Admission routes1
Has abstractyes

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Same topicAdventure Sports and Sensation SeekingFrench-language works237,207