Institutionalizing a global anti-corruption regime: Perverse effects on country outcomes, 1984–2012
Bibliographic record
Abstract
A global anti-corruption movement rapidly mobilized and institutionalized during the mid-1990s. Using data for 119 countries between 1984 and 2012, I examine the effects of this movement on rated levels of perceived corruption. Results from multivariate regression analyses show that the global surge in anti-corruption organizing, monitoring, and legalization was paradoxically associated with an increase in rated levels of corruption, over and above a host of political, economic, social, and cultural factors shown in previous research to explain perceived corruption. With the international standardization, scrutinization, and stigmatization of corruption, activities once hidden from view or previously regarded as ‘standard operating procedure’ came to be denominated, detected, and decried as illegitimate. In turn, these processes gave the impression that corruption worsened, when in fact it may have remained stable or even improved. These findings lend support to institutional approaches in sociology and the ‘information paradox’ concept in political science.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".