Reconsidering Approaches to Aboriginal Science and Mathematics Education
Bibliographic record
Abstract
In Canada, Aboriginal postsecondary enrollment and completion rates are significantly lower than those of non-Aboriginal students. This is most evident in studies involving science and mathematics. The investigation of this issue was informed by focus group discussions with eight participants representing a Blackfoot community. Themes emerging in the data emphasize educational approaches of assimilation, segregation, and mutual dialogue. Intertwining Aboriginal and Western knowledge systems holds generative possibilities for science and mathematics education. Understanding the perspectives of Aboriginal educators in the context of science and mathematics education is a necessary beginning place for future investigations into improving postsecondary completion rates by Aboriginal students. Au Canada, les taux d'inscription et de réussite aux études postsecondaires sont plus faibles, et de façon appréciable, chez les Autochtones que chez les non Autochtones. Cet écart est le plus évident dans les programmes d’études impliquant les sciences et les mathématiques. Notre recherche repose sur des discussions avec des groupes de consultation formés de huit membres qui représentaient une communauté Pieds-Noirs. Les thèmes qui ont découlé des données soulignent les approches pédagogiques touchant l'assimilation, la ségrégation et le dialogue mutuel. L'entrelacement du système de connaissances autochtone et du système de connaissances occidentales offre des possibilités génératives pour l'enseignement des sciences et des mathématiques. À l'avenir, les études portant sur l'amélioration des taux de réussite chez les étudiants autochtones doivent s'appuyer sur une connaissance de la perspective des enseignants autochtones dans le contexte de l'enseignement des sciences et des mathématiques.
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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 | 0.015 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.030 | 0.032 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".