State and Self-Regulation of Civil Society Organizations in Context: A Case Study of Kenya
Bibliographic record
Abstract
Much was made of the global associational revolution beginning in the 1980s, as civil society organizations (CSOs) increased in number worldwide.Since then, a paradox of competing trends has emerged.On one hand, considerable stock is held in the promise of civil society whereby CSOs are recognized as critical actors in economic, social, and democratic development.Governments have sought ways to enable vibrant and diverse CSO sectors through regulation, reflecting a diffusion of pro-CSO norms globally.Deviating from this trend, an associational counter-revolution has arisen whereby governments use regulation to constrain the space for CSO operations.Advancing CSO accountability is a key rationale for many intensified forms of both enabling and constraining state regulation, and self-regulation.Theory suggests there is a trade-off between state and self-regulation, from which cycles of regulatory waves unfold.This dissertation contributes to the CSO regulation literature, which has tended to focus on high-income countries, by developing and applying a conceptual framework of regulatory change drivers to a lower-middle income country, Kenya.Beginning in the late 1980s Kenya was the first African country to significantly address CSO regulation and self-regulation, and has since undergone four phases of CSO regulatory change, sometimes more, sometimes less enabling of the sector.The dissertation draws from primary data in the form of 63 interviews, legislation, regulation, and media sources.Its conceptual framework can help anticipate state and/or self-regulatory change; the state iii and self-regulation interaction; their relationship to accountability; and the associational counter-revolution paradox.This dissertation finds that the most important driver of regulatory change in Kenya's lower-middle income context is government's political agendas, hand-in-hand with the types of activities CSOs engage in.Donor country governments are also an important direct and indirect driver of regulatory change.While regulatory change occurs in 'waves', rather than interplay between state and self-regulation, it is the drivers of regulatory change and their interplay that move and shape the waves.Regulatory change may be turned to as a technical solution to various issues reflected in the drivers, even as it may not necessarily be the best, the only, or sufficient means to address them.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".