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Record W4415590673 · doi:10.29173/cf936

Atteintes à l’environnement naturel et didactisme écologique dans quelques récits ivoiriens pour la jeunesse

2025· article· fr· W4415590673 on OpenAlexvenueno aff
Katienegnimin Seydou Konate

Bibliographic record

VenueConvergences francophones · 2025
Typearticle
Languagefr
FieldSocial Sciences
TopicFrench Urban and Social Studies
Canadian institutionsnot available
Fundersnot available
KeywordsChinese DreamSharecroppingESPACE

Abstract

fetched live from OpenAlex

Yacouba, chasseur africain (1998) d’Ahmadou Kourouma, Le Royaume mystérieux et autres contes de la sagesse (2014) de Seydou Gougna et Le Messager (2016) de Camara Nangala font référence à une zone de biodiversité cruciale, très marquée par la crise écologique : la Côte d’Ivoire. Cet article explore ces trois textes sous l’angle de l’écocritique ou l’écopoétique, afin de mieux cerner les enjeux actuels des défis environnementaux. Kourouma plaide en faveur des savoirs traditionnels sur l’environnement, dans un milieu rural qui se trouve être le plus ébranlé par le changement climatique. Nangala et Gougna vont plus loin, et interrogent ces lieux sur fond de crises environnementales, les œuvres intégrant ainsi une fonction pédagogique et militante. L’étude examine comment, dans les trois textes, le thème de l’environnement naturel fait le lien entre différents lieux. Ensuite, de quelle manière ils problématisent les atteintes faites à cet espace naturel, et la relation qu’ils établissent entre la destruction, la domination ou la résistance des lieux, et la destruction du sacré même, et de traditions, une clé de voûte dans la résolution de la crise climatique.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.050
Threshold uncertainty score0.100

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0080.009
Scholarly communication0.0050.003
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.000

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 head0.015
GPT teacher head0.268
Teacher spread0.253 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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