Socioeconomic Gradient Literacy and Numeracy Skills of 15-year-olds across Canadian Provinces and Years using the PISA Surveys (2000-2012)
Bibliographic record
Abstract
In 2000, the OECD began the Programme for International Student Assessment (PISA), a triennial survey of the knowledge and skills of 15-year-olds. For each survey, Canadian students placed well above the OECD average and remain among the top performers for each domain assessed (reading, math and science). Canada is unique by the very large size of students' samples because education policy is decided by each of ten provincial governments. This paper investigates neglected issues related specifically to 15-year-old students' educational achievement across Canadian provinces. The analysis estimates empirically across provinces the link between the family background, measured by socioeconomic status (SES), and educational skills measured by PISA test scores in reading and math. The SES used is more conventional then the arbitrary character of the index developed by PISA. First, average gaps in students' educational attainment between the lower and top SES quintiles, across provinces and years, provide evidence on the SES gradient in literacy and numeracy competencies. Second, gradients are estimated over the entire achievement distribution (SES gaps over nine deciles) for Canada and across provinces. The third research question relates to proficiency levels and socio-economic gradient, a forgotten subject but a decisive factor for later educational and economic success of young adults. The fourth research question assesses the trends in socio-economic inequalities from the lorgnette of skills measured over five PISA waves (2000 to 2012). Results show large socioeconomic differences in average PISA reading and math scores across provinces. There are wide-ranging variations in the size of score gaps in the SES family background, a proxy for the extent of inequality of opportunities. Quintiles regression estimates expound that the gaps move up and down over the achievement decile scores distribution, and across provinces and waves for both reading and math scores. The association between family background and proficiency levels in both main domain tests is strong, with estimates illustrating significantly large socioeconomic gradients. Summary statistics and estimates on scores changes in bottom and top SES quintiles across provinces suggest that children's reading and math skills are still heavily linked to their family background.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".