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Record W187650169 · doi:10.7202/1032670ar

Alphabétisation et bibliothèques publiques : trois niveaux d’intervention possibles

2015· article· fr· W187650169 on OpenAlexaffvenueabout
Marie-Hélène Fournier, Stéphanie Gagnon

Bibliographic record

VenueDocumentation et bibliothèques · 2015
Typearticle
Languagefr
FieldSocial Sciences
TopicSocial Sciences and Governance
Canadian institutionsMinistère des Ressources naturelles et des Forêts (Québec)Bibliothèque et Archives nationales du Québec
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

Une enquête internationale sur l’alphabétisation des adultes a révélé, en 1994, qu’environ 28 % de la population québécoise est analphabète et qu’entre 54 % et 60 % des Québécois se retrouvent en deçà du seuil d’alphabétisation minimal, nécessaire à un bon fonctionnement en société. Comparativement aux bibliothèques publiques ontariennes et américaines, les bibliothèques québécoises font figure de parents pauvres quand il s’agit de répertorier leurs initiatives en alphabétisation. En effet, leurs actions se font rares, ou du moins, trouvent peu d’échos dans la littérature professionnelle. Afin de contrer cette situation, les auteures décrivent trois programmes représentant trois niveaux d’intervention potentiels, qui visent à mettre sur pied des services à l’intention des analphabètes, des nouveaux alphabétisés et des organismes voués à l’alphabétisation dans les bibliothèques publiques. En somme, il s’agit de propositions d’action qui peuvent être adaptées et appliquées aux bibliothèques désireuses de contrer le phénomène de l’analphabétisme au Québec. Toutefois, les auteures de l’article n’analysent pas la faisabilité de ces programmes qui doit être examinée à la lumière des ressources disponibles.

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)0.027
metaresearch head score (Gemma)0.078
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: none
Teacher disagreement score0.544
Threshold uncertainty score0.918

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0270.078
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.010
Science and technology studies0.0100.005
Scholarly communication0.0100.006
Open science0.0030.007
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0240.002

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 head0.386
Teacher spread0.335 · 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".

Quick stats

Citations4
Published2015
Admission routes3
Has abstractyes

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