Corruption in the public schools of Europe: A cross-national multilevel analysis of education system characteristics
Bibliographic record
Abstract
Researchers have long theorized that characteristics of education systems impact both perceived and experienced corruption in public schools. However, due to insufficient cross-national survey data with measures on corruption in education and unassembled yet publicly available institutional data, there are few empirical tests of this theory. This article provides the rare direct test of the relationship between corruption in European public schools and three education system factors: government expenditure on education, education staff compensation, and teacher workload (pupil–teacher ratio). With a newly constructed harmonized data set for European countries, and controlling for national economic factors and individual characteristics, results of multilevel analyses suggest partial support for the theory that specific institutional characteristics of education systems impact public school corruption. The theorized institutional factors have different effects that depend on whether we examine bribe-giving experience or corruption perception. Results show that bribe-giving experience in public schools of Europe is weakly yet significantly related to education staff compensation. For corruption perception, low levels of government expenditure on education and a lopsided pupil–teacher ratio (too few teachers per student) increase the probability that people view corruption as prevalent.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".