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Record W4313988021 · doi:10.57054/ad.v47i4.2983

Enjeux de la pédagogie contrastée de l’histoire dans les sous-systèmes anglophone et francophone pour les politiques mémorielles au Cameroun

2023· article· fr· W4313988021 on OpenAlexaff
Nadeige Ngo Nlend, Ludovic Ladǒ, Gishleine D. Oukouomi, Ewane Etah, Eric Acha

Bibliographic record

VenueAfrica Development · 2023
Typearticle
Languagefr
FieldSocial Sciences
TopicAfrican Studies and Ethnography
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

Reposant sur la collecte des données empiriques et sur la recherche documentaire, l’article compare l’enseignement de l’histoire du Cameroun dans les sous-systèmes francophones et anglophones à partir de l’analyse de supports pédagogiques et didactiques variés. Il questionne la place que revêt le Cameroun dans la discipline historique de niveau secondaire ainsi que la manière dont y sont traitées certaines séquences de son passé. Si les programmes d’histoire du premier cycle que partagent les deux sous-systèmes mentionnent bien le Cameroun à certains niveaux d’enseignement, le volume horaire ainsi que l’ampleur des sujets traités sont de loin plus élevés dans le sous-système anglophone. Par ailleurs, alors que les manuels d’histoire, limités au premier cycle dans le sous-système francophone, font l’impasse sur les thématiques relatives à la construction de l’État du Cameroun, les manuels anglophones inscrits au premier cycle et au second cycle y consacrent de larges extraits. Sans toutefois postuler une relation de cause à effet, l’article tente une exploration des enjeux de cette pédagogie contrastée pour les politiques mémorielles au Cameroun, au coeur du réveil d’un protonationalisme anglophone qui donne lieu à des relectures contrastées de l’histoire du Cameroun.

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)0.007
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 score0.125
Threshold uncertainty score0.249

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0060.008
Scholarly communication0.0040.003
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.000

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 head0.053
GPT teacher head0.318
Teacher spread0.265 · 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
Published2023
Admission routes1
Has abstractyes

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