The Institutional and Community Capacity for Aboriginal Education: A Case Study
Bibliographic record
Abstract
O’owe zagakibii’igewin da-ozhibii’igaade bezhig noomaya’ii gii-maajichigaade Anishinaabewi dago mooniyaawi-gikina’amaagewin omaa Kaanada akiing. Ogikina’amaageg gaa-wiiji’iwewaad omaa izhichigewining gii-anishinaabewiwag, gii-waabishkiiwewag gaye. Gichi-anishinaabeg gaye ningoding gii-aabadiziwag. Aaniin ji-izhi-minosegiban, awe gaa-niigaanishkang ogii-gaganoonaa’ bebakaan awiya’ gaa-gikina’amaageng zhigwa gii-ayizhaa bekish wii-zagakibii’iged. Iwe zagakibii’igewin izhisin, eniwek gii-gwaawaanjichigewag gegoo dazhiikigaadeg, wiiji’iweg ayaawaad dago awiyag gechiwaag imaa eyaawaad. Endasing ono gwaawaanjichigewinan, gegoo zenagakin zhigwa gegoon ge-wiiji’iwesegin, gikenjigaade zhigwa aaniin ezhi-gichi-inendaagwak Anishinaabewi-gikina’amaagewin aaniin gaye ezhi-zanagak oodenaang ji-dazhi-gikina’amaageng This study explored the institutional and community capacity for Aboriginal education in one urban school district in Canada that recently established a formal Aboriginal education programme. Participants in this study were Aboriginal and non-Aboriginal education professionals, as well as Aboriginal Elders directly and indirectly affiliated with the study school district. Using a qualitative approach, I conducted interviews with participants and observed in-school activities in an effort to acquire data. I found that the physical, political, human resource, and community capacities were, in varying degrees, sufficiently developed to provide Aboriginal education programming. Within each of these capacities, challenges and perceived opportunities have led to an acknowledgement of the importance of Aboriginal education as well as an appreciation of the difficulties of offering such programming in an urban educational environment.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.020 | 0.010 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".