Bibliographic record
Abstract
Why does the United States lag behind so many other countries on international education assessments? The traditional view targets school-based explanations—U.S. schools attract poorer teachers and lack the proper incentives. But the U.S. educational system may also serve children with comparatively greater academic challenges as a result of poorer social conditions. One way of gaining leverage on this issue is to understand when U.S. students fall behind their international counterparts. I first compare reading/vocabulary test scores for U.S. and Canadian children (ages 4-5) using National Longitudinal Study of Youth 1979–Children and Youth (NLSY79) and Canada’s National Longitudinal Study of Children and Youth (NLSCY). I then compare the magnitude of these differences to similar cohorts of students at ages 15 to 16 using data from the Programme for International Student Assessment (PISA). Findings indicate that while the Canadian advantage in PISA is substantial (0.30 standard deviation units), this advantage already existed at ages 4 to 5, before formal schooling had a chance to matter. I discuss the implications of this pattern for interpreting international test score rankings.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".