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Record W2900040192 · doi:10.23860/jmle-2018-10-03-08

Empowering Indigenous Learners through the Creation of Graphic Novels

2018· article· en· W2900040192 on OpenAlexafffundabout
Deborah L. Begoray, Alexis Brown

Bibliographic record

VenueJournal of Media Literacy Education · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicLiteracy, Media, and Education
Canadian institutionsUniversity of Victoria
FundersCanadian Institutes of Health Research
KeywordsIndigenousComicsMedia literacyComprehensionThe artsConfusionNarrativeLiteracyPedagogyPsychologySociologyMedical educationMedicineVisual artsComputer scienceArtLiterature

Abstract

fetched live from OpenAlex

In this paper, we examine how Indigenous and non-Indigenous adolescents identify media influences as health/wellness related. We conducted research over a six-week period in two alternative high school settings: a culture-based Indigenous education program at one school and an arts-based program at another school, both in the same small, Western Canadian city. We taught students from both programs the principles of critical media health literacy. Small groups of students from the Indigenous program wrote narratives. Then small groups of Indigenous and non-Indigenous students in an arts-based education program converted these stories into graphic novel/comic book format. Findings indicated a broad range of health/wellness topics discussed, media stereotypes challenged, and varying levels of comprehension about media’s impact on health. These levels ranged from misunderstanding or confusion through developing general understanding and, at the highest level, specific understanding.

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 score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0060.010
Scholarly communication0.0070.003
Open science0.0010.007
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.001

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 head0.023
GPT teacher head0.318
Teacher spread0.295 · 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

Citations10
Published2018
Admission routes3
Has abstractyes

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