Teacher Attrition in a Northern Ontario Remote First Nation: A Narrative Re-Storying
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it