Bibliographic record
Abstract
“Good schools ” in Ontario have principals, teachers and other staff who are making a positive difference in student performance, regardless of their students ' socio-economic backgrounds. This study screens out the influence of socio-economic factors on how a school's students perform on Ontario’s standardized tests at the end of Primary Division (Grade 3) and Junior Division (Grade 6). This allows the author to identify those schools that perform better or worse than other schools with students of similar backgrounds. The resulting school ratings by percentile are useful not only to parents, but also to school board administrators and education officials who wish to identify schools whose practices deserve imitation. 1 Similar school ratings are published by the C.D. Howe Institute for schools in Alberta and British Columbia. How do parents, teachers, taxpayers and school administrators know if children are attending a good school? Standardized test results in reading, writing and mathematics offer one way when analyzed appropriately. This e-brief answers that question with a methodology that filters out the influence of socio-economic
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.096 | 0.014 |
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".