Strengthening the Knowledge, Skills, and Professional Identity of Early Educators: The Impact of the California SEIU Early Educator Apprenticeship Program
Bibliographic record
Abstract
Ensuring that early educators have the skills, knowledge, and experiences necessary to implement effective practices and the access to education that provides wage improvement remains critical. Quality improvement leaders and policymakers have increasingly considered alternative instructional and training models to deliver education, including the apprenticeship model, which is gaining favor with many in the early care and education field. This model combines classroom-based learning and on-the-job training to provide the knowledge and skills early educators need in order to implement effective practices in their early education roles. In 2019, the Center for the Study of Child Care Employment (CSCCE) conducted an evaluation of the SEIU Early Educator Apprenticeship Programs. This evaluation adds to the growing body of evidence that apprenticeship programs present a promising approach to improving the knowledge, skills, and professional identity of early educators. Apprentices who participated in this evaluation benefited from the strategies employed by the apprenticeship programs to remove barriers and support success, and these apprentices reported gains in their knowledge and enhancements to their practices with children and families. The report offers recommendations for future iterations of apprenticeship and on-the-job training programs.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".