Determinants of educational achievement of Francophone students in Ontario
Bibliographic record
Abstract
Ontario students' results on national and international assessments reveal a pattern: Francophone students in Ontario usually perform worse than Anglophone students. Recognizing the importance of acquiring reading literacy skills, this study focuses on performance of Ontario students in the Progress in International Reading Literacy Study 2001 (PIRLS) conducted by the International Association for the Evaluation of Educational Achievement (IEA). Using an expanded version of the Trends in International Mathematics and Science Study (TIMSS) model, this research explores, in addition to the intended curriculum, the implemented curriculum and the attained curriculum, the assessment and the students' performance. The analysis, comparing Ontario Anglophone students and Francophone students' performance and contextual data, uncovers important differences in each of the factors explored. The Ontario French-language and English-language curriculum, the students' prior knowledge, and the classroom environment, including teaching practices and resources, showed notable differences. In addition, the Anglophone and Francophone students' test-taking behaviours and responses were very different. The difficulty of the language used in the assessment and the scoring of the assessment also differed between the English- and French-language versions of the PIRLS. These analyses illustrate the complexity of comparisons across school systems, even those within the same province, and the importance of caution in such comparisons.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".