MétaCan
Menu
Back to cohort
Record W3000684964 · doi:10.1002/curj.5

Literacies and identities in transnational education: a case study of literacy curricula in a Canadian transnational education programme in China

2019· article· en· W3000684964 on OpenAlexafffundabout
Zheng Zhang, Rachel Heydon, Wanjing Li, Pam Malins

Bibliographic record

VenueThe Curriculum Journal · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsWestern University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsCurriculumLiteracyPedagogyMainland ChinaTransformative learningSociologyIdentity (music)ChinaCritical literacyPolitical scienceMathematics educationPsychology

Abstract

fetched live from OpenAlex

This ethnographic case study focused on a transnational education programme in an inner city in Mainland China that used both Chinese high school curriculum and Canadian provincial curriculum from New Brunswick. The goal of the study was to capture the desires and power relations that shaped literacy and identity options in the school's hybrid curriculum. Findings revealed the affordances of the programme in expanding students' cultural and linguistic knowledge and capabilities in two languages and the constraints to their literacy and identity options. Notable constraints included the compartmentalization of English and Chinese curricula, standardized literacy tests, and the school policies that limited teachers' incorporation of new media literacies and critical literacy. The study contributes to extant knowledge of transformative transnational literacy education that could help educators provide pedagogical opportunities for students to construct fluid and multi‐layered identities that connect to their complex, multilingual literacy practices.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.131
Threshold uncertainty score0.310

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0320.011
Scholarly communication0.0040.002
Open science0.0020.006
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.019
GPT teacher head0.410
Teacher spread0.391 · 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

Citations13
Published2019
Admission routes3
Has abstractyes

Explore more

Same venueThe Curriculum JournalSame topicMultilingual Education and PolicyFrench-language works237,207