Variation in socioeconomic gradients among cantons in French- and Italian-speaking Switzerland: Findings from the OECD PISA
Bibliographic record
Abstract
Results from the 2000 Organization for Economic Cooperation and Development (OECD) Programme for International Assessment (PISA) indicated that inequalities in performance associated with students' family background were relatively large in Switzerland compared to other participating countries. The study upon which this article is based examines the relationships between literacy performance and family background for Switzerland in greater detail. Particular attention is given to the variation in this relationship among the German-, French-, and Italian-speaking jurisdictions, and among cantons within the French- and Italian-speaking regions. The study uses data for 6,100 15-year-old students who participated in the main PISA study, and for 5,730 students who participated in a supplemental study of grade-9 students in the French- and Italian-speaking regions. The analysis employs multilevel statistical techniques to examine the relationships among students within classrooms, and among classrooms within cantons. The findings that emerged from the study have important implications for school policy in Switzerland.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".