ABORIGINAL PERSPECTIVES AND THE SOCIAL STUDIES CURRICULUM
Bibliographic record
Abstract
We are still not having [Aboriginal] people succeed in the mainstream education system ... the numbers have not changed, so something is not right (Participant, Antone & Cordoba, 2005, p. 5). Aboriginal peoples are an important part of Canada. With approximately 72 languages and 614 First Nations reserve communities, there is a richness of knowledge, tradition and customs, and depth in epistemological (ways of knowing) and ontological (ways of being) understanding that can add to the wealth of this country. However, with so much potential for reciprocal learning, why is “something not right” in education for so many Aboriginal students? This literature review will explore the following question: “To what extent do teacher attitudes, norms, values, basic assumptions, and behaviour influence authentic inclusion, infusion, and embedding of Aboriginal perspectives in the Alberta Social Studies Program?” While the knowledge and skills preparation of a teacher directly influences the quality of teaching and impacts student learning, the authors go beyond knowledge and skill development to explore how teacher attitudes and perspectives influence learning and the teaching of Aboriginal perspectives. Furthermore, effective teacher practice and strategies (e.g. culturally responsive teaching) are described.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.016 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".