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Bibliographic record
Abstract
The Early Development Instrument (EDI) was designed by McMaster University to measure the outcomes of children's early years as they influence their readiness to learn at school. The EDI was piloted in several Canadian cities in recent years through two national initiatives. Building on these initiatives, Alberta Learning piloted the EDI as a possible measure of readiness to learn at school in two school jurisdictions in 1999-2000 and in five jurisdictions in 2001-2002. This study evaluated Alberta Learning's pilot project, examining the efficacy and utility of the EDI for teachers' use in classroom programming and instructional decision making. Semistructured interviews were conducted with samples of teachers (n=39) and administrators (n=4)from the 5 jurisdictions from 2001-2002. Most participants found the EDI easy to use and viewed the organization and support provided for its administration as "very adequate. " The orientation sessions were regarded positively, and release time to study the instrument was considered extremely helpful. Some suggestions for improvement were developed, including more case studies, and distributing the instrument earlier in the year. (Contains 43 references.) (SLD) Reproductions supplied by EDRS are the best that can be made from the original document.
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 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.855 | 0.794 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".