Corruption as a Diagnostic of Power: Navigating the Blurred Boundaries of the Relational State
Bibliographic record
Abstract
In this article, we bring ethnographic insights from the contemporary moment of social and political transformation in Nepal to bear on troubling established understandings of corruption. We argue that corruption furnishes a productive site from which to interpret the relational space of state practice and to probe the ‘blurred boundaries’ of states, polities and markets in everyday lives. Reading corruption as a ‘diagnostic of power’, moreover, can help reveal intersecting and competing structures of power at work in ongoing processes of state construction and contestation. The argument is developed through an examination of three related and overlapping illustrations of public–private transgression, which we characterise as ‘impossible publics’, ‘consensus collusion’ and ‘patronage democracy’. Taken together, they speak to the diversity of practices commonly glossed as corruption, and their embeddedness in powerful mobilisations of affective communion and intimate relations of mutual obligation. We suggest in conclusion that engaging corruption as a diagnostic of power offers critical insights about the nature of, and possibilities for, distribution, political agency and planning. Overall, we make a case for the importance of disaggregating diverse practices of corruption in specific, socially embedded contexts in order to reveal possibilities for the meaningful redistribution of political power and opportunity.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.100 |
| Scholarly communication | 0.013 | 0.024 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.002 | 0.005 |
| 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".