Teacher Attrition in a Northern Ontario Remote First Nation: A Narrative Re-Storying
Bibliographic record
Abstract
Increasing teacher retention in First Nations communities has been identified in the literature as requiring attention. When attrition rates are high and teacher efficacy, quality of student experience, and overall academic achievement is compromised, efforts to mobilize plans for stability are needed. Through a narrative re-storying approach this paper unpacks the challenges and opportunities related to teacher attrition in one remote First Nation community in Northern Ontario. Although teacher attrition is inevitable, it is necessary to re-envision attrition factors as a plan for retention. Community integrated induction and mentorship programming, and continuous and multi-year contracts are two possible approaches to boost retention. Teacher education is also explored as a long-term approach to address teacher attrition from a system perspective. In all approaches, collaborative effort, engagement, and funding are needed from the federal government, local education authorities, and faculties of education to increase teacher retention in remote First Nation communities.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.004 |
| 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".