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Record W2018894699 · doi:10.5430/elr.v2n1p86

Performance of Francophone Secondary School Leavers in English Structure and Written Expression

2013· article· en· W2018894699 on OpenAlexvenueno aff
Jean‐Paul Kouega, Steohane Celeste Piewo Sokeng

Bibliographic record

VenueEnglish Linguistics Research · 2013
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsSyllabusCertificateTest of English as a Foreign LanguageMathematics educationSchool CertificateTest (biology)Subject (documents)FrenchPedagogyEnglish languagePsychologySociologyLinguisticsComputer scienceLibrary science

Abstract

fetched live from OpenAlex

This study looks into the performance in English of Francophone learners in general, and into their mastery of structure and written expression in particular. The informants were 430 pupils who had just obtained the Baccalauréat , the certificate required for admission into tertiary education, and who came from different schools in ten regions of the country. The data were a collection of answers to a TOEFL-like test designed to check these pupils’ knowledge of English structure. The analysis revealed that these pupils did not attain college level English, and the English test they took at the Baccalauréat did not seem to effectively assess knowledge of structure. To ensure that Francophones do perform well in English, there is a need to re-orient the English language syllabus and to use the time allocated to the English subject (3 hours a week for seven years) to teach them the substance of English-medium primary education content. When this is done, they would be able to take the First School Leaving Certificate examination before they complete French-medium secondary education.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.026
GPT teacher head0.280
Teacher spread0.254 · 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 designObservational
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
Published2013
Admission routes1
Has abstractyes

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