An Exploration of the Effects of Mentor-Apprentice Programs on Mentors' and Apprentices' Wellbeing
Bibliographic record
Abstract
Increasingly, adult Indigenous language learners are being identified as the “missing generation” of learners who hold great potential to contribute to the revival of Indigenous languages by acting as the middle ground between Elders, children, and youth within their communities. Our research project NEȾOLṈEW̱ “one mind, one people” studied adult Indigenous language learning through the popular Mentor-Apprentice Program method. Over a 2-year period, our team conducted interviews and focus groups with participants involved in a Mentor-Apprentice type program in British Columbia, Canada. While our primary interest was to document the successes and challenges of the Mentor-Apprentice Program method for adult Indigenous language learning, we also included interview questions that gave participants an opportunity to share how participating in such a program affected them. During data analysis, we noticed repeating comments from participants about how their involvement with a Mentor-Apprentice Program impacted their own and their community’s wellbeing; 6 exploratory themes were identified. Although studies have reported protective effects of Indigenous language use on health, health-related outcomes of language revitalization efforts remain underexplored. In addition to discussing the exploratory themes that arose from the study, our paper also proposes that these themes can inform future research in investigating the links between language revitalization and wellbeing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".