Approaching Educational Empowerment: Guidelines from a Collaborative Study with the Innu of Labrador
Bibliographic record
Abstract
This paper discusses the journey toward self-managed education for the Innu people of coastal Labrador who, after an arduous struggle, have finally attained autonomy from the Canadian government. While the paper briefly explores the broader context within which Innu education has evolved, particular attention will be given to the role served by a recent research project in both documenting the specific educational needs of the people and presenting a process to guide change. What emerged from that study was a wealth of data including community attitudes to education, as well as indicators of attendance, ability and achievement of the entire population of school-aged children. The study documented significant learning needs among the school-aged population despite average cognitive ability and a desire to achieve well in school. A plethora of policy recommendations was presented to guide the creation of Innu-managed education as well as to establish a template for the creation of a bicultural model of education, one in which traditional culture and native language were prioritized. This paper explores the five-year impact of that study on both policy and practice for Aboriginal education in coastal Labrador. As such, it informs the establishment of policy and pedagogical approaches for educators attempting to balance contemporary educational opportunity with retention of core cultural values.
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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 | 0.209 | 0.105 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.023 | 0.026 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.010 | 0.031 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".