Can the Early Development Instrument be a possible assessment tool for the perceived government literacy development objectives at Ontario Early Years Centres
Bibliographic record
Abstract
This study examined whether the Early Development Instrument (EDI) (McMaster University, Hamilton Health Sciences Corporation, 2000) could be an effective assessment tool for the perceived government curriculum objectives at the Ontario Early Years Centres (OEYC). The sample consisted of four girls and two boys, aged 3--4 years. All of the subjects attended the same OEYC Centre, located in Windsor, Ontario. The Early Childhood Educators in the OEYC completed pre- and post-tests of the Early Development Instrument on each of the subjects. The researcher was an observer in the OEYC for two months. During this time, observational field notes were taken on the six subjects. The actions of each subject at the OEYC site, as well the words spoken by the subjects was recorded by the researcher. The researcher also spent time exploring the City of Windsor Early Learning and Family (ELF) Centres curriculum, which the Windsor area Ontario Early Years Centres were using, while they awaited the arrival of a province-wide, government generated curriculum. (Abstract shortened by UMI.) Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2003 .S46. Source: Masters Abstracts International, Volume: 42-02, page: 0380. Adviser: Kara Smith. Thesis (M.Ed.)--University of Windsor (Canada), 2003.
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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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".