UNDERSTANDING SUCCESS IN COMMUNITY FIRST NATION EDUCATION THROUGH ANISHINABE MENO-BIMAADZIWIN ACTION RESEARCH
Bibliographic record
Abstract
As involuntary minorities within their own ancestral lands, Canada’s First Nation people have experienced trauma within western-based education systems such as federal residential and day schools, public, and independent schools, which has resulted, in many cases, in social devastation and economic disparity. An indigenous and community approach to education and research promises to provide a solution to such intergenerational problems. This research provides compelling evidence that improvement to First Nation education is possible when initiated at the local community level. This is an important discovery as most non-native policies and initiatives designed to improve education for native people have failed. A community-based culturally relevant definition of success in education has been demonstrated to bring about increased levels of critical awareness that provide incentive for an informed response that contributes toward improvements to education for First Nation people at the community level. This research involved my entry into the Chippewas of the Thames First Nation community as a researcher, possessing both “insider” and “outsider” status, initiating relationships with people based upon our commonalities of culture and roles in society. In efforts to facilitate increased awareness levels in response to areas of difficulty within education, I facilitated a community-based inquiry using a culturally-based methodology that I have termed Anishinabe Meno-Bimaadziwin Action Research. The results were successful in that participation within the community-designed and community-driven research led to increased awareness and incentive for transformative action in iii education at levels that surpassed community involvement within other initiatives that were not community-based. The results of this research provide insight into the potential that exists within a First Nation community to devise culturally relevant solutions to challenges that occur within education. These findings support the validity and effectiveness of community-based development and initiatives in education, and in other areas of local community development within First Nation communities. These findings provide a strong argument in favour of increased native jurisdiction and self-government initiatives at the local community level.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| 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".