School socioeconomic status and student outcomes in reading and mathematics: A comparison of Australia and Canada
Bibliographic record
Abstract
Previous research has established that student outcomes are strongly associated with the socioeconomic composition of a school, also known as school socioeconomic status. Less is known, however, about the ways in which the relationship varies for different students, schools and national education systems. Here, we conduct a secondary analysis of an international dataset to examine the strength of the relationship between school socioeconomic status and achievement in math and reading for Canada and Australia. The history, economy and culture of these two countries are similar, as are many aspects of their education systems. One important difference, however, is the degree to which their education systems are marketised. Our findings show that in both countries, school socioeconomic status is strongly associated with academic achievement for all students, regardless of their individual socioeconomic status. Nevertheless, the relationship between school socioeconomic status and academic achievement is substantially stronger in Australia than in Canada. We conclude that student outcomes are more equitable in Canada than in Australia, and suggest that this may be due to differences in the ways in which the two education systems are funded and structured.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".