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Record W2610214878 · doi:10.7202/1034865ar

« Apprendre pour mieux s’organiser ». Une expérience d’alphabétisation au Mali

2016· article· fr· W2610214878 on OpenAlexvenueno aff
Jules Savaria

Bibliographic record

VenueInternational Review of Community Development · 2016
Typearticle
Languagefr
FieldAgricultural and Biological Sciences
TopicAgriculture and Rural Development Research
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical scienceHumanitiesArt

Abstract

fetched live from OpenAlex

Depuis 1975 se déroule dans la zone sud du Mali un projet d’alphabétisation caractérisé par une volonté d’insérer les apprentissages de base dans un plan de développement économique et social géré par des organisations villageoises populaires. L’auteur discute du bienfondé d’une alphabétisation dans les langues nationales; il insiste sur la nécessité de définir la fonctionnalité de l’alphabétisation par rapport aux chances objectives de l’utilisation de cet apprentissage qui sont directement liées à la proximité (ou non) des villages des zones de développement et de modernisation. Enfin l’auteur soutient qu’il est indispensable d’accorder une attention suffisante au rôle des organisations locales, relais des stratégies d’alphabétisation. Les trois dimensions : formation — animation — organisation sont inséparables. L’éducation n’est pas à elle seule moteur de changement. L’efficacité des programmes de formation dépend directement de leur lien aux transformations du système économique et social.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.104

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.007
Scholarly communication0.0050.003
Open science0.0010.007
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0160.002

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 head0.038
GPT teacher head0.300
Teacher spread0.262 · 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 designQualitative
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
Published2016
Admission routes1
Has abstractyes

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Same venueInternational Review of Community DevelopmentSame topicAgriculture and Rural Development ResearchFrench-language works237,207