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Record W6997167250

Using Digital Technologies to Address Aboriginal Adolescents' Education: An Alternative School Intervention

2009· article· en· W6997167250 on OpenAlexaboutno aff

Bibliographic record

VenueScholarworks (University of Massachusetts Amherst) · 2009
Typearticle
Languageen
FieldArts and Humanities
TopicLiteracy, Media, and Education
Canadian institutionsnot available
Fundersnot available
KeywordsMainstreamSituatedCurriculumDigital inclusionDigital literacyLiteracyInclusion (mineral)EthnographyIntervention (counseling)
DOInot available

Abstract

fetched live from OpenAlex

Purpose – The purpose of this paper is to examine how digital technologies were introduced in a collaborative literacy intervention to address a population long underserved by traditional schools: the Aboriginals of Canada. Design/methodology/approach – Situated within a critical ethnographic project, this paper examines how digital technologies were introduced. The questions focused on: how can critical multiliteracies be used to engage students, in both academic and digital literacies development? In what ways does participation in multimodal media production provide evidence of teachers and students' critical literacy development? Findings – Digital literacies as a part of multiliteracies were developed in teaching contexts where learning is challenged by many factors. Research limitations/implications – The paper reports on the achievement and the struggles that remain. Implications for further research and teacher education are also drawn from the experience of implementing a broader definition of literacy in academic settings with Aboriginal students of Canada. Originality/value – The inclusion of a digital curriculum provides possibilities for greater academic success for marginalized students in both mainstream and alternative schools.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.397
Threshold uncertainty score0.851

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.004
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.280
Teacher spread0.254 · 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 teacher head, 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

Citations1
Published2009
Admission routes1
Has abstractyes

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