Bibliographic record
Abstract
Abstract How does quantifying and ranking national performance influence state behavior? Cross-national assessments in education, such as the Programme for International Student Assessment (PISA) promoted by the Organisation for Economic Co-operation and Development (OECD), have become increasingly prominent in recent years. However, cross-national assessments are politically contentious, and their impact remains underexplored. We argue that assessment participation has a meaningful, positive impact on education outcomes and evaluate three hypotheses related to elite, domestic, and transnational mechanisms. Our mixed-method approach draws on a panel dataset covering all cross-national assessments and all countries as well as an original survey of education officials directly responsible for planning and implementation in 46 countries. We find that assessment participation increases net secondary enrollment rates even after accounting for potential self-selection. The magnitude of this increase is large: on a global basis, it is equivalent to improved access to higher education for 27–32 million students annually. The empirical evidence suggests elite-level mechanisms are primarily responsible for these findings.
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 | 0.179 | 0.258 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".