e-brief Heads of the Class: A Comparison of Ontario School Boards by Student Achievement
Bibliographic record
Abstract
Does differing management of schools by Ontario school boards affect student outcomes? This e-brief uses the approach and data I developed in Johnson (2005, 2007) to answer that question. While my previous work provided a method for evaluating and comparing individual school performance based on student achievement, this study focuses on evaluating the performance of entire school boards. I conclude that there are significant differences among school boards in terms of student achievement. As a starting point, I employ data provided by Ontario’s standardized test results in reading, writing and mathematics in grades 3 and 6. These results, however, reflect both the quality of teaching at the school and the socio-economic characteristics of the school’s community. Schools where parents have lower socio-economic profiles will have fewer students meet or exceed expectations (herein referred to as a pass) regardless of teacher or administrator effort and ability. Adjusting test scores to remove the influence of these socio-economic factors (which explain about 40 percent of the variation among schools) yields measures of relative school performance that represent a school’s effectiveness. The relationship between a socio-economic index and a school’s adjusted pass rate is shown in Figure 1 for an illustrative group of Grade 3 schools. 1
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 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".