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Record W2912256707 · doi:10.1186/s41039-019-0095-2

Appropriation of affordances of multiliteracies for Chinese literacy teaching in Canada

2019· article· en· W2912256707 on OpenAlexaffabout
Mi Song Kim, Xiaotong Xing

Bibliographic record

VenueResearch and Practice in Technology Enhanced Learning · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicLiteracy, Media, and Education
Canadian institutionsWestern University
FundersNational Research Foundation of Korea
KeywordsAffordanceAppropriationLiteracyPedagogyNarrativeCurriculumTeaching methodSociologyNarrative inquiryMathematics educationPsychologyLinguistics

Abstract

fetched live from OpenAlex

Abstract Innovative literacy teaching and learning approaches have proved that multiliteracies enables teachers to work with culturally and linguistically diverse (CLD) students. However, few studies have shed light on examining Chinese teachers’ understanding and practices of multiliteracies in Canada. To respond to this challenge, this qualitative case study aims to understand how Chinese teachers who pursued multiliteracies graduate studies perceive and implement multiliteracies in teaching young CLD children Chinese literacy in Canada. We investigated narratives of two Chinese teachers to provide useful insights into their lived experiences of multiliteracies for Chinese literacy teaching. A constant comparison approach was adopted to analyze three data sources through narrative analysis: interviews, reflective writings, and curriculum materials. Findings suggest that these teachers actively appropriated affordances of multiliteracies for Chinese literacy teaching in Canada drawing upon graduate courses in multiliteracies. The paper concludes with the pertaining implications to highlight the importance of teacher professional development.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.084
Threshold uncertainty score0.236

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0100.005
Scholarly communication0.0040.001
Open science0.0010.005
Research integrity0.0000.001
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 head0.025
GPT teacher head0.368
Teacher spread0.344 · 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

Citations26
Published2019
Admission routes2
Has abstractyes

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