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Record W2099926968 · doi:10.5897/err.9000101

Developing multiliteracies through bilingual education in Burkina Faso

2008· review· en· W2099926968 on OpenAlexaff
Constance Lavoie

Bibliographic record

VenueEducational Research Review · 2008
Typereview
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsMcGill University
Fundersnot available
KeywordsIndigenousLiteracyBilingual educationContext (archaeology)SociologyNeuroscience of multilingualismIdentity (music)Qualitative researchPedagogyPsychologyGeographyAnthropology

Abstract

fetched live from OpenAlex

Being literate involves being able to move from the ability to read and write to include different forms of knowledge and modes of communication of the milieu (drumming, dancing, story-telling, etc.). This article examines the new literacy allowed through the existence of two types of schools (bilingual and monolingual) in Burkina Faso, in West Africa, and how the two affect the development of literacy and cultural sustainability of their graduates. Since 1994, this country has moved from a French only educational system inherited from colonization to a bilingual one. In this context, bilingual education means the learning of two languages (African language and French) and the indigenous knowledge and ways of learning. The data is based on a qualitative study conducted during 2006 and 2007 in this country. Twenty semi-structured interviews were conducted with participants from bilingual schools and from monolingual schools. They analyze the impact of their schooling path on their literacy development and cultural identity by looking at the language spoken and written, and the information they use and produce.   Key words: Multiliteracies, postcolonial education, Africa.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.612
GPT teacher head0.684
Teacher spread0.073 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations23
Published2008
Admission routes1
Has abstractyes

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