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Record W2982446238 · doi:10.3917/gs1.160.0061

Aide-mémoire externe et vieillissement : bien plus qu’une aide pour se rappeler

2019· article· fr· W2982446238 on OpenAlexaboutno aff
Amandine Porcher-Sala, Camille Beaurain, Marion Sinoquet

Bibliographic record

VenueGérontologie et société · 2019
Typearticle
Languagefr
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

Les dispositifs d’aide-mémoire externe (AME), comme les agendas électroniques par exemple, représentent des technologies utiles pour répondre aux besoins liés à la mémoire et l’organisation de la vie quotidienne des aînés. Certains de ces objets sont conçus pour fournir également un bénéfice sur le plan social ou affectif. Mais est-ce qu’un aide-mémoire peut réellement offrir une utilité étendue au-delà de sa fonction d’assistance cognitive ? Pour répondre à cette question, deux études complémentaires ont été conduites : une étude théorique à partir d’une revue de la littérature et une étude empirique des usages des AME auprès d’aînés en France et au Canada. Les résultats permettent de dégager une classification de neuf fonctions utiles, individuelles et/ou collectives, des AME. Ils ouvrent à plusieurs implications pour que les concepteurs de telles technologies prennent en compte les besoins, les fonctionnements biopsychosociaux, la créativité dans l’usage et l’environnement des aînés futurs utilisateurs.

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)0.011
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.088
Threshold uncertainty score0.174

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0030.006
Scholarly communication0.0070.008
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0090.001

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 head0.072
GPT teacher head0.369
Teacher spread0.298 · 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
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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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