Application of Multilevel Latent Class Analysis to Identify Achievement and Socio-Economic Typologies in the 20 Wealthiest Countries
Bibliographic record
Abstract
There has been increased interest in cross-national comparisons of educational achievement, particularly using the data provided through the Programme for International Student Assessment (PISA). The growing tendency in the popular media is to characterize such comparisons by ranking nations based upon mean achievement test scores. However, recent work has demonstrated that the way in which students are organized in schools has a impact on student achievement. The current study demonstrated the utility of a relatively new statistical technique, the nonparametric latent class model, to investigate the cross national organization of schools and its relationship to student achievement and socio-economic status. The results demonstrated that indeed, it is not enough to simply compare mean achievement performance across countries, but rather that the ways in which nations organize students into schools is also associated with test performance. The results of this study highlight both the importance of understanding school organizational context, and the analytic power of the nonparametric latent class model.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".