Essential Policy Intelligence | Conseils indispensables sur les politiques SOCIAL POLICY Warning Signs for Canadian Educators: The Bad News in Canada’s PISA Results
Bibliographic record
Abstract
The Program for International Student Assessment (PISA) conducts core competency tests that are solid predictors of future economic growth – more so than “input measures, ” such as average years of schooling. PISA assesses the academic ability of 15-year-olds across three subject areas – reading, mathematics and science. While Canada’s outcomes remain well above the OECD average, they have been slipping. Every three years since 2000, PISA has administered tests in these three subject areas, with a rotating focus. The focus for the latest round, in 2012, was on mathematics, with fewer questions posed on the other two subjects. From the respective “base years ” to 2012, Canada experienced statistically significant declines in two of the three subject areas, science and mathematics. There are wide provincial variations in outcomes. For example, Quebec has avoided a decline in its mathematics score over the last decade while all other provinces have seen declines. Prince Edward Island and Manitoba have experienced statistically significant declines in all three subjects. The 2012 results rarely mentioned the relative ability of Canadian schools to overcome the education disadvantage of students from families with low socio-economic status. Canada ranked fifth among OECD countries in terms of minimizing the negative impact of low socio-economic status on mathematics scores. Education is much more than training the next generation’s labour force, but the contribution to economic prosperity from a well-run primary and secondary school system is undeniable. There is solid evidence that economic growth in any country is a function of the academic abilities of This E-Brief benefited from rigorous review by both C.D. Howe Institute analysts and external analysts, some of whom wish to be anonymous. I thank Marie-Anne Deussing for her detailed review of the manuscript. Colin Busby and James Fleming contributed editorial advice and organized preparation of the E-Brief.
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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 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.019 | 0.010 |
| Scholarly communication | 0.020 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.011 | 0.017 |
| Insufficient payload (model declined to judge) | 0.023 | 0.002 |
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".