The employment, retention and exit of public school teachers in New Brunswick, Canada: an analysis using linked administrative data
Bibliographic record
Abstract
ObjectiveAs in many jurisdictions, New Brunswick (Canada) is facing increasing shortages of K-12 school teachers as retirements loom at the same time that the school age population of NB continues to exceed long term trends. The purpose of this retrospective study is to analyze the recruitment, retention and exit decisions of teachers in the NB public education system in order to support ongoing planning around teacher staffing. ApproachThe analysis uses a unique linked administrative data combining province-wide individual-level teacher employment data, immigration records, university graduation data and public health insurance registration on NB teachers and individuals who obtained a B.Ed. degree in NB. Data are accessed through the secure facilities of the NB Institute for Research, Data and Training. The analysis includes both descriptive statistics and econometric methods appropriate to the particular outcomes of interest. ResultsThe analysis will present results on three types of labour market transitions: 1) from NB university education to employment as a NB teacher. 2) exits from employment as a teacher, including both retirement and pre-retirement exits, and 3) decisions of ex-teachers about remaining in NB. The potential effects of a range of demographic, geographic and system-level factors on these outcomes are considered. Conclusions and ImplicationResults from the analysis of entry to and exit from K-12 teaching in NB will be vital to human resource planning for a province dealing with looming retirements, growing labour shortages and an increasing population. Future work will consider subject-specific teacher shortages in critical fields like STEM.
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How this classification was reachedexpand
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 | 0.014 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".