Using digital technologies to address Aboriginal adolescents' education
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".