Informal Governance: Comparative Perspectives on Co-optation, Control and Camouflage in Rwanda, Tanzania and Uganda
Bibliographic record
Abstract
This article applies a novel conceptual framework to characterise and assess the repertoire of practices used by informal networks to redistribute power and access to resources. These distinct norms and practices are typologised as co-optation, control, and camouflage. Co-optation involves recruitment into the network by means of the reciprocal exchange of favours. Control is about ensuring discipline amongst network members by means of shaming and social isolation. Camouflage refers to the formal facades behind which informality hides and is about protecting and legitimising the network. All three are relevant to a more fine-grained understanding of corruption and its underpinnings. Findings from our comparative research in three East African countries (Rwanda, Tanzania and Uganda) suggest that these informal practices are relevant to an understanding of the choices and attitudes of providers and users of public services at the local level. Adopting this analytical lens helps to explain the limited impact of conventional anti-corruption prescriptions and provides a basis to develop alternative strategies that harness the potential of social network dynamics to promote positive anti-corruption outcomes.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".