Identifying British Columbia’s Best Schools Education Papers
Bibliographic record
Abstract
In this issue... The study identifies which B.C. elementary schools are worthy of emulation — and those where large improvements are possible — by removing the influ-ence of local socioeconomic factors on a school’s performance. The Study in Brief Standardized testing is a controversial subject, particularly in British Columbia. However, as this Commentary argues, standardized test results can be a valuable resource as long as they are placed into the proper context. It is no surprise that students who have parents with more education or speak English as a first language do far better on standardized tests than otherwise disadvantaged students. This Commentary compares outcomes in British Columbia schools where students come from similar backgrounds. Professor David Johnson’s methodology, based on his ground-breaking study of Ontario schools, identifies which schools are doing better or worse than expected given the socioeconomic characteristics of their students. In British Columbia slightly over half of the variation in tests scores is associated with variation in
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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 | 0.010 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.018 | 0.003 |
| Scholarly communication | 0.019 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.066 | 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".