How Integrating Aboriginal Perspectives into the Classroom Affects Students Attitudes Towards Aboriginal People
Bibliographic record
Abstract
This paper explores the methods employed by Alberta Education to teach Alberta students about the Indigenous Peoples of Canada. Currently, Alberta Education has two approaches, which are: 1) the integration of the First Nations, Metis, and Inuit Policy Framework (FNMI), which is a framework that is a means to educate all Albertans on the history of Aboriginal Peoples, and 2) an optional Aboriginal Studies coursework. An urban high school participated in this research study, which was under the call for the integration of the FNMI policy framework and also offered Aboriginal Studies 10. I used a Blackfoot theoretical framework, grounded in an Indigenous research methodology, alongside principles of the Beadworking paradigm to conduct the research. I employed a survey that was quantitative in nature to determine students’ attitudes towards the Indigenous Peoples of Canada. I was interested in identifying whether taking Aboriginal Studies 10 made a difference in the participants’ views of Indigenous Peoples. I used principal-component factor analysis and multivariate analysis of variance (MANOVA) to analyze the data. The results from the MANOVA analysis indicate that the Aboriginal Studies 10 class plays a role in students’ perceptions of Indigenous Peoples specifically. These results indicate that students who participated in the Aboriginal Studies 10 course had a more positive view of Indigenous Peoples than students who did not participate in Aboriginal Studies 10.
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 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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".