Bibliographic record
Abstract
Remote First Nations across Canada face many challenges related to the provision of quality education and training programs for local citizens. First Nations across northwestern Ontario are using digital technologies and broadband infrastructure to deliver a full range of education and provide training opportunities for formal and informal learning for their citizens. This paper examines my research and policy development work and relationship with remote First Nations as a result of a long-term partnership. Community surveys conducted in 2014 and 2016 and follow-up interviews of residents in five remote First Nations were completed exploring online teaching, education, and professional development opportunities available and what their experiences are with these opportunities. The data also examines the programs, support systems and online services remote community residents desire. Follow up data was collected in 2015 and 2016 during community visits to one of the remote communities. Through a settler colonialism lens and action research approach, I present the importance of supporting First Nation control of their education systems and delivery systems through research and appropriate First Nation-led policy development work supporting services and innovative opportunities in remote communities.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.045 | 0.018 |
| Scholarly communication | 0.021 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.017 |
| Insufficient payload (model declined to judge) | 0.017 | 0.010 |
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".