Assessing Program Delivery from the Perspective of Service Providers: the Ontario Early Years Centres’ School Readiness Program
Bibliographic record
Abstract
This paper assesses the School Readiness program as delivered in two Ontario Early Years Centres (OEYCs) in Toronto. Information about program goals and delivery methods gleaned from interviewing three Child Development Consultants who ran the program in the last year is analyzed within the context of existing scholarship on and practices in early childhood learning and development. The interviewers' observations and comments form the basis for the assessment of the effectiveness of the OEYC School Readiness program. An important part of assessing effectiveness is determining whether the OEYCs and program workers have set clearly defined learning outcomes for program participants and how, if at all, the program measures these outcomes. In assessing program effectiveness, one of the factors considered is to what extent the OEYCs acknowledge and address the needs of an important demographic: immigrant children (and their support network of parents/caregivers and families). The observations and recommendations made in this study are intended to help service providers in the OEYCs develop a best practice model for program delivery, including arriving at a better sense of how they conceive of school readiness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".