The Education Papers School Class Size: Smaller Isn’t Better
Bibliographic record
Abstract
In this issue... Many provinces are spending millions of dollars on class-size reduction initiatives, with no solid evidence that they raise student achievement. The money could be better spent elsewhere. The Study in Brief Many provinces, most notably Ontario, are going ahead with class size reduction initiatives in the early primary grades. Many parents and teachers are strong advocates of reducing class sizes in the later primary and secondary grades, as well. However, in spite of the fact that such policies are often promoted as means to help students perform better, there is no solid base of empirical evidence to show that smaller classes improve student achievement beyond kindergarten and grade one, when pupils are being socialized into the classroom environment. Even in those very first school years, the gains in achievement observed are relatively small and do not carry through to later years. Recent standardized test scores from Canadian pupils aged 13 to 16 years old show no evidence that smaller classes are better, either for achievement or classroom atmosphere. One possible, though surely partial, explanation for the counter-intuitive effect is that school systems that have smaller classes have to employ more teachers than otherwise, forcing them to hire less-qualified teachers. There is some evidence that this effect is at play in Canada. Because reducing class size is enormously expensive, it is very likely that the money being spent there could be better spent on other educational policies, such as continuous teacher training, which, unlike class size reduction, have been shown to improve student performance.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.014 | 0.013 |
| Insufficient payload (model declined to judge) | 0.098 | 0.030 |
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".