CSO s in Sustainable Development in Ethiopia: Past Practices and New Trajectories
Bibliographic record
Abstract
Abstract We researched how CSO s working in the area of sustainable development responded to regulatory restrictions on advocacy work using Ethiopia as a case study. We found that the restrictive laws had a severe impact: many CSO s had to shut down or limit their operational capacity to service delivery only. Those that survived continued to do advocacy work, disguised as service delivery. This shows that northern stakeholders should not adhere to a strict division between advocacy and service delivery in their funding policy. They also should focus on long-term CSO engagement and long-term CSO funding. In 2019, regulatory reform reopened political space to some extent. The new law envisions a greater role for self-regulation in the civil society sector while still maintaining some degree of State oversight through registration, reporting and funding allocation requirements. Despite these improvements, the sector is still in need of international support and consistent and reliable funding.
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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 | 0.001 |
| 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) | 0.000 | 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".