Political culture and the resource curse: public sector corruption across the United States
Bibliographic record
Abstract
Purpose This study aims to examine the connection between political culture and public sector corruption, using the typology of Daniel Elazar, whose model traces the types of political cultures to their origins in various regions of England. Similarly, the “resource curse” concept, generally treated as a national-level phenomenon, is examined to assess how it might vary among jurisdictions within a country. Design/methodology/approach Regression analysis was applied to data from the 50 states of the US. Public sector corruption in each state was operationalized as the number of convictions by the Public Integrity Section of the US Department of Justice in relation to the number of public sector employees in that state. Findings Among the 50 states of the US, support was found for the association between political culture and public sector corruption. On the other hand, whether a state’s economy was dominated by natural resource extraction was not related to public sector corruption. This latter finding suggests the “resource curse” phenomenon does not cause corruption to be worse in states with resource-dependent economies. Research limitations/implications Although it is appropriate to apply regression analysis to a data set of the 50 US states, the small size of the data set limited the number of predictor variables that could be examined. Alternative research approaches are discussed, and it is conceivable that another analytical technique might have revealed other predictors that affect the occurrence of corruption. Originality/value While numerous studies have examined the impact of political culture and resource orientation on corruption at the national level, the current study examines how these variables affect corruption at the level of subnational jurisdictions within a major developed country, the United States.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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 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".