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Record W4385771837 · doi:10.4148/0146-9282.2345

Teaching French as a Foreign Language in Multilingual and Anglophone Contexts: The Experiences of Teachers in Nigeria and Ghana

2023· article· en· W4385771837 on OpenAlexaff
Michael Akinpelu, Stella Afi Makafui Yegblemenawo

Bibliographic record

VenueEducational Considerations · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicFrench Language Learning Methods
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsCurriculumFrenchForeign languagePolitical scienceSubject (documents)Language policyOfficial languageDeveloping countryPedagogySociologyEconomic growthLinguisticsLibrary scienceEconomicsLaw

Abstract

fetched live from OpenAlex

Nigeria and Ghana are two Anglophone countries in West Africa that have adopted the teaching of the French language in their education systems because of their proximity to francophone countries and the necessity for regional integration. Whereas the language has gained some official status in the national curriculum (National Policy on Education) in Nigeria and made a required subject at some levels of education, French continues to enjoy a privileged status in Ghana but without an official status yet. Using a comparative approach, this paper explores the language policy in favour of the French language and its teaching at the secondary and post-secondary levels in both countries. The analysis of primary data collected with teachers at both levels in these countries show that, although French does not enjoy the same status in the two geographical spaces, similarities abound in terms of the policy regulating its teaching and the challenges impeding the full implementation of the policy. Country-specific solutions are offered to existing challenges.

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)0.004
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 score0.031
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0160.010
Scholarly communication0.0060.003
Open science0.0010.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.029
GPT teacher head0.382
Teacher spread0.353 · 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

Citations5
Published2023
Admission routes1
Has abstractyes

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