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Record W3206997973 · doi:10.7202/1083422ar

Enacted Agency in a Cross-Border, Online Biliteracy Curriculum Making: Creativity and bilingual digital storytelling

2021· article· en· W3206997973 on OpenAlexaffvenueabout
Zheng Zhang, Wanjing Li

Bibliographic record

VenueMcGill Journal of Education / Revue des sciences de l éducation de McGill · 2021
Typearticle
Languageen
FieldHealth Professions
TopicDigital Storytelling and Education
Canadian institutionsWestern University
Fundersnot available
KeywordsCreativityAgency (philosophy)SociologyPedagogyDigital storytellingTransformative learningNetnographyPosthumanismPsychologySocial mediaPolitical scienceSocial scienceAestheticsSocial psychology

Abstract

fetched live from OpenAlex

This research investigated potentials of bilingual digital story making to engage the creativity of 13 Canadian and Chinese biliteracy learners aged 11–15. Findings in this paper draw on six focal participants and their digital story creation. Informed by asset-oriented multiliteracies, new media literacies, and new materialism, this research adopted a netnography methodology to explore the communal and sociomaterial practices embedded in the intra-actions of human, matter, and virtual spaces of Seesaw and Skype. Drawing on data from six focal students, findings relate how intra-actions among researchers, teachers, students, matters, and spaces shaped participants’ creative acts. This research adds to the knowledge of developing and applying material-informed pedagogies which attend to the enacted agency among teachers, students, materials, and spaces.

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)0.004
metaresearch head score (Gemma)0.007
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 score0.015
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.009
Scholarly communication0.0070.003
Open science0.0010.008
Research integrity0.0010.001
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 head0.299
GPT teacher head0.541
Teacher spread0.242 · 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

Citations6
Published2021
Admission routes3
Has abstractyes

Explore more

Same venueMcGill Journal of Education / Revue des sciences de l éducation de McGillSame topicDigital Storytelling and EducationFrench-language works237,207