Inter-connecting Aboriginal and Western Paradigms in Post-secondary Science Education: An Action Research Approach
Bibliographic record
Abstract
Very few Aboriginal[1] students are successful in science courses in the Western education system, particularly at the post-secondary level. Without a science background they are excluded from entering into science-related professions such as medicine, scientific research, science education, fields of engineering environmental and biological sciences to name a few. The result has been the severe under-representation of Aboriginal people and their voice in such professions. The vast array of literature that addresses the issue of Aboriginal success in post-secondary education is quantitative in nature and done “on” Aboriginal people by non-Aboriginal people. Very little qualitative data exists, that addresses this issue from the experiential voice and perspective of the Aboriginal people themselves. This paper addresses the results of focus group discussions with Aboriginal students, faculty, teachers and community members around the issue, from their perspective and voice, of what is needed for Aboriginal success in post-secondary education with a specific focus on science. [1] The research for this project was done in collaboration with members of the surrounding Blackfoot community, the Aboriginal community indigenous to Southern Alberta. The words Aboriginal, Indigenous, Native will be used interchangeably within this paper and are meant to be inclusionary of Aboriginal peoples. Blackfoot specifically refers to members of the surrounding Aboriginal community involved in this project.
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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 | 0.038 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.026 | 0.023 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".