Peer Spillover and Big-Fish-Little-Pond Effects with SIMS80: Revisiting a Historical Database Through the Lens of a Modern Methodological Perspective
Bibliographic record
Abstract
Abstract The present study uses doubly latent models to estimate the effect of average mathematics achievement at the class level on students’ subsequent mathematics achievement (the “Peer Spillover Effect”) and mathematics self-concept (the “Big-Fish-Little-Pond-Effect; BFLPE”), controlling for individual differences in prior mathematics achievement. Our data, consisting of 13-year-old students from Canada, the USA, and New Zealand, come from a unique cross-national database with a longitudinal design at the student level: the Second International Mathematics Study (SIMS80). This historical survey was administered by IEA in the 1980s and highly influenced the development of educational policies in the following decades. We replicate a widely cited study based on SIMS80, interrogating the validity of its findings of a positive peer spillover effect. When we adjust for measurement error, using doubly latent models, we observe that originally positive peer spillover effects become less positive or disappear altogether. On the contrary, negative BFLPEs become more negative and remain statistically significant throughout. Our study is the only cross-national study to have evaluated both the BFLPE and the peer spillover effect with controls for a true measure of prior achievement — and the only study to test the peer spillover effect cross-nationally using doubly latent models. Our findings question the empirical results of past and current research evaluating school- and class-level compositional effects based on sub-optimal models that fail to control for measurement error.
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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 | 0.022 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 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".