Does the EDI Measure School Readiness in the Same Way Across Different Groups of Children?
Bibliographic record
Abstract
The present study investigates whether the Early Development Instrument (Offord & Janus, 1999 Offord, D., Janus, M. (1999). Early Development Instrument. A population-based measure for communities (2004/05 version). Retrieved November 20, 2006 www.offordcentre.com/readiness/EDI_viewonly.html [Google Scholar]) measures school readiness similarly across different groups of children. We employ ordinal logistic regression to investigate differential item functioning, a method of examining measurement bias. For 40,000 children, our analysis compares groups according to gender, English-as-a-second-language (ESL) status, and Aboriginal status. Our results indicate no systematic measurement differences regarding Aboriginal status and gender, except for 1 item on which boys are more likely than girls to be rated as physically aggressive by Kindergarten teachers. In contrast, ESL children systematically receive lower ratings on items of the language and communication domains—as expected by definition of ESL status—but not within the physical, social, and emotional domains. We discuss how our results fit with child development research and the purpose of the Early Development Instrument, thus supporting its validity.
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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 | 0.017 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".