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Record W2587497363 · doi:10.7202/1033994ar

Vieillir comme on a vécu : la clé pour décoder la vieillesse de demain

2015· article· fr· W2587497363 on OpenAlexaffvenue
Nicole Marcil‐Gratton

Bibliographic record

VenueInternational Review of Community Development · 2015
Typearticle
Languagefr
FieldHealth Professions
TopicAging, Elder Care, and Social Issues
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsHumanitiesArtPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

Le vieillissement démographique est à nos portes. Rien ne pourra désormais empêcher que les têtes blanches occupent une part de plus en plus importante dans la communauté. Cependant, si vieillir est un processus embrayé depuis la naissance dans la vie de chacun, c’est aussi, pour la société, le résultat d’un cheminement déjà bien inscrit dans les caractéristiques de sa population encore jeune. Pour mieux comprendre ce que sera la société vieillie, il faut éviter de faire le jeu du miroir en projetant pour l’avenir l’image que nous transmet la vieillesse d’aujourd’hui. Cet article explore la vie des générations encore jeunes dans le but de déceler des indicateurs d’une amélioration possible de l’état dans lequel on franchira désormais le seuil du troisième âge.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.628
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.147
GPT teacher head0.457
Teacher spread0.310 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations1
Published2015
Admission routes2
Has abstractyes

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