Do Economic Equality and Generalized Trust Inhibit Academic Dishonesty? Evidence From State-Level Search-Engine Queries
Why this work is in the frame
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Bibliographic record
Abstract
What effect does economic inequality have on academic integrity? Using data from search-engine queries made between 2003 and 2011 on Google and state-level measures of income inequality and generalized trust, I found that academically dishonest searches (queries seeking term-paper mills and help with cheating) were more likely to come from states with higher income inequality and lower levels of generalized trust. These relations persisted even when controlling for contextual variables, such as average income and the number of colleges per capita. The relation between income inequality and academic dishonesty was fully mediated by generalized trust. When there is higher economic inequality, people are less likely to view one another as trustworthy. This lower generalized trust, in turn, is associated with a greater prevalence of academic dishonesty. These results might explain previous findings on the effectiveness of honor codes.
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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 | 0.010 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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