Native Language Education: An Inquiry Into What Is and What Could Be
Bibliographic record
Abstract
The objective of my honors thesis study was to explore the current state ofK-12 andpostsecondary Native language (NL) education in Canada and to examine thepossibilities of incorporating outdoor education with NL programs. This qualitativeresearch project explored this objective through focus group sessions and individualinterviews. Learner and Educator participants were queried about their experiences,values, and ideas about Native language education. Twenty-two Aboriginal adultsparticipated in my study, and results pointed to a number of conclusions: (a) learnersappear to wish to increase their Native language skills; (b) educators appear to beinterested in working toward reaching educational ideals; (c) much work remains torevitalize Native languages in Canada; and (d) gaps in Native languageprogramming suggest opportunities to develop and implement more Native languageprograms including the incorporation of outdoor education.
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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 | 0.014 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.022 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".