MétaCan
Menu
Back to cohort
Record W3041370664 · doi:10.1075/ltyl.19017.hay

Language education policy and practice in state education systems

2020· article· en· W3041370664 on OpenAlexaff
David Hayes

Bibliographic record

VenueLanguage Teaching for Young Learners · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsBrock University
Fundersnot available
KeywordsForeign languageCurriculumContext (archaeology)Language policyPedagogyAffect (linguistics)State (computer science)Political scienceMathematics educationPsychologySociologyComputer scienceGeography

Abstract

fetched live from OpenAlex

Abstract This article considers the complexity of factors involved in developing foreign language educational policy, with a particular focus on English at the primary level, which fosters student achievement in state educational systems. It examines both those factors which underpin a successful education system in general (such as equitable provision for all socio-economic groups within the society) as well as factors which affect language teaching policy and practice for primary school learners in particular (such as a curriculum which offers teachers and children opportunities to engage in language use which is meaningful in their contexts). The status and training of highly skilled foreign language teachers for primary aged learners is also accorded due weight. Using case studies of English as a foreign language in the school systems of South Korea and Thailand, the article emphasizes the importance of policies which are appropriate to their educational and socio-cultural context and realistic objectives for young children’s early engagement with learning a foreign language if it is to be a positive experience in primary schools.

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)0.008
metaresearch head score (Gemma)0.011
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.135

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.012
Scholarly communication0.0140.005
Open science0.0010.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0070.000

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 head0.034
GPT teacher head0.465
Teacher spread0.431 · 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

Citations4
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueLanguage Teaching for Young LearnersSame topicMultilingual Education and PolicyFrench-language works237,207