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 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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".