The long‐run educational benefits of high‐achieving classrooms
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This paper examines how placement in high‐achieving classrooms within high school impacts students’ short‐ and longer‐term academic outcomes. Our setting is a large and selective Chinese high school, where first‐year students are separated into high‐achieving and regular classrooms based on their performance on a standardized exam. Classrooms differ in terms of peer ability, teacher quality, and class size, as well as level and pace of instruction. Using newly collected administrative data and a regression discontinuity design, we show that high‐achieving classrooms improve math test scores by 23% of a standard deviation, with effects persisting throughout the 3 years of high school. Impacts on performance in Chinese and English language subjects are more muted. Importantly, we find that high‐achieving classrooms raise enrollment in elite universities by 17 percentage points, as they substantially increase scores on the national college entrance exam—the sole determinant of university admission in China. We provide suggestive evidence that the most likely mechanism driving our results is exposure to higher‐quality teachers.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it