Towards the development of contextual questionnaires for the PISA for development study
Bibliographic record
Abstract
The aim of this paper is to describe the technical issues to be addressed in enhancing the Programme for International Student Assessment (PISA) contextual questionnaires instruments for the PISA for Development (PfD) study. We discuss the conceptual framework for the contextual questionnaires used in PISA, describe the evolution of the PISA contextual questionnaires, review the measures used in several other international studies, and consider how the PISA data have been used to address the policy questions relevant to the OECD member countries. This research, alongside discussions with key stakeholders, including those from participating countries, enabled us to identify seven themes in which the PISA contextual questionnaires could be enhanced and made more relevant for low- and middle-income countries: early learning opportunities, language at home and at school, family and community support, quality of instruction, learning time, socioeconomic status, and school resources. We discuss various options for enhancing these measures.
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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 | 0.358 | 0.364 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".