Diversity, Economic Characteristics, and Retention of Early Learning and Child Care Workers in Canada
Bibliographic record
Abstract
Given Canada’s increasingly diverse population, a better understanding of the representation of different groups among Canadian workers and their economic characteristics is needed. This study analyzes the diversity, economic characteristics, and retention of workers in Canada’s early learning and child care (ELCC) sector. Using census data, diversity characteristics (e.g. sex, visible minority status such as race and ethnicity, language, and activity limitations) were compared between ELCC and non-ELCC workers. The study also examines whether ELCC workers’ economic characteristics, such as self-employment rate, full-time work status, employment income, and poverty rates, varied by diversity groups. It then investigates whether diversity characteristics were associated with workers’ retention in ELCC occupations. Findings showed an over-representation of women, visible minorities, and non-native speakers of Canadian official languages in the ELCC workforce. Generally, non-White ELCC workers were more likely than their White counterparts to be paid employees, work full-time, and earn less than $40,000 annually. Variations in ELCC workers’ economic characteristics and retention rates were also found across visible minority groups. Findings from this study provide greater insight into the representation and retention of different groups within ELCC occupations, which can contribute to higher quality early learning and child care for Canada’s multicultural population.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".