From Candidate to Teacher: Strategies for Teacher Education Programs to Ensure Employment for Indigenous Teacher Graduates
Bibliographic record
Abstract
This presentation summarizes a research project that gathered and analyzed different strategies by Teacher Education Programs (TEPs) to ensure that Indigenous teacher candidates were successfully obtaining and maintaining positions as teachers, following graduation. The study gathered data from 50 Universities or TEPs from across Canada, and select examples from the US, Australia, and New Zealand. Data was organized and analyzed according to theme, to produce three objectives for TEPs in supporting Indigenous teacher graduates in new teaching positions. The strategies are: creating teaching positions, identifying community needs/collaborating over practicum placements, and providing ongoing support. The presentation will conclude with an additional research finding, which highlights the need to continue to ethically research, collect, assess and use data in order to further improve upon strategies for increasing the number of Indigenous teachers. Ultimately, the research is premised on the need for more Indigenous teachers in schools across Canada, and our presentation seeks to provide University, TEP and College educators, coordinators and planners with strategies to consider for their own programs.
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 | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.021 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".