Canadian ECEC labour shortages: big, costly and solvable
Bibliographic record
Abstract
Canada’s early childhood education and care (ECEC) sector is primarily under the jurisdiction of the provinces and territories. Each jurisdiction has its own distinct set of regulations, programmes and policies. These differences result in a diverse mix of employment settings, training requirements and availability of regulated childcare places. Despite the myriad of approaches there are a number of striking similarities in the ECEC labour market outcomes throughout Canada. The sector faces low pay, high staff turnover and persistent workforce shortages. ECEC workforce shortages are extremely costly given the short- and long-term benefits delivered by quality ECEC. The dynamics of Canada’s ECEC labour market are unique compared with other nations. Parents are price sensitive, labour supply is extremely responsive to wage increases and governments regularly short-circuit labour market outcomes. Expansion of services in this sector often leads to a lessening of quality as more inexperienced staff are taken on. This chapter illustrates the magnitude of workforce shortages in the Canadian ECEC sector and explores market oriented and public oriented solutions to end Canada’s recruitment and retention crisis.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".