The Early Development Instrument: Translating School Readiness Assessment Into Community Actions and Policy Planning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This invited special issue of Early Education and Development presents research related to the Early Development Instrument (EDI; CitationJanus & Offord, 2007), a community tool to assess children's school readiness at a population level. In this editorial introduction, we first sketch out recent trends in school readiness research that call for a contextual and whole-child assessment of school readiness. Then we provide an overview of the EDI, including a discussion of its purpose and development, as well as its large-scale international use as a community tool to monitor children's developmental outcomes at population levels. Finally, we introduce the special issue's articles, all of which present research findings from ongoing community research projects that employ the EDI to assess children's school readiness. These articles are grouped into the following thematic themes: (a) individual-level validity of the EDI, (b) school and neighborhood effects and population-level validity of the EDI, and (c) program implementation and evaluation using the EDI. Notes 1The EDI Web site is www.offordcentre.com/readiness/ 2The Human Early Learning Partnership (HELP) is a multi-university, interdisciplinary research consortium led by Clyde Hertzman at the University of British Columbia, Canada, that is dedicated to early childhood research and practice in collaboration with communities and government. HELP (www.earlylearning.ubc.ca) has been designated the World Health Organization's Knowledge Hub for early child development.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it