Alignment of a high-ranked PISA mathematics curriculum and the<i>Parallel Curriculum</i>for gifted students: Is a high PISA Mathematics ranking indicative of curricular suitability for gifted learners?
Bibliographic record
Abstract
Quebec students have generally excelled in international mathematics comparisons and 22% performed in the top category, Level 6, on PISA in 2012. Several countries with more extensive gifted programs scored and ranked considerably lower and had smaller proportions achieving Level 6. Does this mean a general mathematics curriculum with such indices of success could sufficiently serve gifted students? The US NAGC’s Parallel Curriculum model served as a template to explore Quebec’s ninth-grade mathematics curriculum for components of the four Parallel Curriculum strands: core, connections, practice, and identity. The Quebec curriculum included a strong core, but fewer elements of the three other Parallels. The anomaly remains: A strong core curriculum was associated with high PISA scores and rankings, yet did not meet all the criteria for gifted programming. At the same time, even though the literature reports that formal gifted programming is sometimes associated with higher proportions of learners achieving at PISA’s level 6, such provision is not as well related to overall high PISA averages or rankings.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".