What factors are associated with public corruption perception? Evidence from Canada
Bibliographic record
Abstract
Purpose Corruption perception is essential to study because it can shape people’s attitudes toward the government. Thus, the purpose of this paper is to address this key question: what factors are associated with a non-expert’s judgment of whether Canada is corrupt? Design/methodology/approach This study uses the World Value Survey conducted in Canada in October 2020. This survey is based on a nationally representative sample of a cross-section of adult Canadian residents, including Canadian citizens and permanent residents and those who are neither Canadian citizens nor permanent residents. Findings Based on this study, some conclusions can be made. First, people accessing corruption news from the traditional news media are less likely than those receiving information from the new media to perceive the state (in this case, Canada) as corrupt. Second, people who have less confidence in public institutions are more likely to perceive a country as corrupt. Third, people who participate in electoral and non-electoral forms of political participation are more likely to perceive the state and its public officials as corrupt. Fourth, regardless of which political party is in power, individuals who lean right politically are more likely than those on the left to perceive the state as corrupt. Finally, immigrants are less likely than those born in Canada to perceive the state as corrupt. This work enriches the literature on the substantive understanding of the factors associated with corruption perception. Originality/value Studies investigating factors associated with public perception of corruption tend to focus on developing countries. The current study contributes to filling this gap in knowledge by examining correlates of corruption perception in Canada. As a result, this study contributes to the literature on factors associated with corruption perception, especially in the developed country context.
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How this classification was reachedexpand
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".