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Record W3106147158 · doi:10.1080/15391523.2020.1809034

#Digital parents: Intergenerational learning through a digital literacy workshop

2020· article· en· W3106147158 on OpenAlexafffund
Cristyne Hébert, Kurt Thumlert, Jennifer Jenson

Bibliographic record

VenueJournal of Research on Technology in Education · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicLiteracy, Media, and Education
Canadian institutionsUniversity of British ColumbiaYork UniversityUniversity of Regina
FundersMinistère de l’Éducation, Gouvernement de l’Ontario
KeywordsAgency (philosophy)Digital literacyEllPedagogyLiteracyImmigrationDigital mediaSociologyPopulationTeaching methodComputer sciencePolitical scienceSocial scienceWorld Wide WebVocabulary development

Abstract

fetched live from OpenAlex

In this article, we present findings of a research study centered around a 10-week digital production workshop developed specifically for families in an urban school board, a population rich with culturally diverse immigrant families and English language learners (ELLs). The aim of this research was to support parents/guardians in an urban community in their development of a practical, hands-on understanding of twenty-first century literacies, using a ‘production pedagogy’ framework that emphasizes learner agency. We sought to critically reflect, alongside parents/guardians, on how new media and new literacies are being utilized in schools today, and to provide models, tools and practices for parents/guardians and their children to enact digital competences together, specifically, through the production of a short digital story.

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)0.005
metaresearch head score (Gemma)0.006
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 score0.011
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0050.002
Scholarly communication0.0040.003
Open science0.0010.007
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0110.002

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 head0.086
GPT teacher head0.401
Teacher spread0.315 · 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

Citations18
Published2020
Admission routes2
Has abstractyes

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