Teacher Supply and Demand: Issues in Northern Canada
Bibliographic record
Abstract
This two‐year study (2007‐2009), which examined teacher supply and demand issues in northern Canada – Fort Nelson School District (BC), the Fort Vermilion School Division (AB), the Yukon Department of Education (YK), and the Yellowknife School District (NWT) – comprised three research objectives: (a) to ascertain in which subject areas acute and chronic needs for teachers existed, (b) to investigate recruitment and retention methods for northern professionals, and (c) to ascertain preferred professional development models. The participants included teachers, principals, and hiring personnel in the research sites. Research methods included the use of an on‐line questionnaire (n = 113), at least two semi‐structured interviews at each of the four sites with five to ten teachers, three to five principals, and one Human Resources personnel, and researcher field notes. Findings confirm and expand upon conclusions drawn by others and demonstrate the unique needs of northern educators. In particular, we found that (a) school districts continue to struggle with finding specialist teachers in the senior high sciences and mathematics and in elementary Special Education, (b) few incentives exist for teachers to come to northern school districts and to stay there, and (c) the preferred professional development model for approximately one‐quarter of these Northern teachers, administrators, and hiring personnel is blended learning.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.015 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".