Teacher Hiring Practices in Atlantic Canada: Processes, Practices, Issues and Challenges
Bibliographic record
Abstract
In the recent past, the discourse around teacher oversupply has intensified as evidence has emerged in several published reports about a widening gap between the number of unemployed or underemployed teacher graduates and the availability of permanent full-time classroom positions in some regions of the country. Some provinces (e.g., Ontario) pulled back on admissions, reduced funding and made other changes to teacher education programs leaving universities to adjust and downscale the ways in which they organize and deliver pre-service teacher education (Government of Ontario, 2013). In this mixed methods study I investigate teacher hiring in the Atlantic Canadian context. I provide an overview of the teacher labour market in Atlantic Canada and explore the difficulties in establishing a reliable forecast of teacher demand and review the specific circumstances of the Atlantic context, where the pattern of enrolment decline began much earlier than elsewhere in the country. I also report on the perspectives of senior school district personnel on a range of issues associated with the teacher hiring processes and practices of educational authorities.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.019 | 0.007 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".