The Difference That One Year of Schooling Makes for Russian Schoolchildren
Bibliographic record
Abstract
The PISA 2009 data (in reading) investigated the effectiveness of one year of schooling in seven countries: Russia, Czech Republic, Hungary, Slovakia, Germany, Canada, and Brazil. We used an instrumental variable, which allowed us to estimate the effect of one year of schooling through the fuzzy method of regression discontinuity. The analysis was performed both for regular and vocational education programs collectively as well as individually for regular schools. It was found that in general for Russian students enrolled in all programs, the effectiveness of one year of schooling is insignificant. In countries that practice the early separation of students into regular and vocational programs, the effectiveness of schooling is lower than in countries where all fifteen-year-olds are enrolled in regular programs. The effectiveness of one year of schooling for students enrolled in regular educational programs is significant in all countries. Students enrolled in vocational programs typically perform more poorly than those enrolled in regular programs. The strength of the relationship between the socioeconomic status of the student's family and the effectiveness of schooling are highly dependent on the education system and vary from country to country. For Russia, as well as for some other countries, the effectiveness of schooling does not depend on socioeconomic status. The significance of these results for the evaluation of the effectiveness of schooling, and in particular for the fair evaluation of national achievement in countries that offer different educational trajectories, is discussed.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".