Class Size and Teacher Work: Research Provided to the BCTF in their Struggle to Negotiate Teacher Working Conditions
Bibliographic record
Abstract
This paper presents an update of a 2010-literature review on class size research completed as background in preparation of an affidavit on class size provided by the lead author in the case of British Columbia Teachers’ Federation v. British Columbia, argued before the Supreme Court of British Columbia in 2010, appealed ultimately to the Supreme Court of Canada and ruled on November 10, 2016. We find that smaller classes can improve teacher-student interactions and individualized instruction, decreasing time spent on discipline issues, leading to better student behaviour, attitude, and efforts. Smaller classes generally have greater advantages for younger students, and effects are more observable in class sizes of less than 20. Small classes may shrink achievement gaps, decrease dropout rates, and increase high school graduation rates, and appear to enhance academic outcomes, particularly for marginalized groups. Researchers have detected class size effects many years later. Small classes have been found to boost teachers’ morale and job satisfaction. While some studies have found effects at the secondary and post-secondary level, results are generally inconclusive at this level. Finally, some researchers have argued that class size reductions are an inefficient use of funds which might be better spent elsewhere in the system. The paper concludes with a brief reflection on the process of providing this research for Supreme Court case.
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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 | 0.017 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".