Non-governmental organizations and development in the Sudan: Relations with the state and institutional strengthening.
Bibliographic record
Abstract
3.2.4NIF Failure: Is it a Failure of the Project or the Regime?3.2.4.1 Islamic Civil Society Movement 3.2.4.2 Regime's Violations and Corruption 3.3 NIF -Civil Society/NGOs Relations 3.3.1 Sudanese Women under the State 3.3.2Restrictions of Voluntary Work 3.3.3The Impact of Peace 3.4 The Positions and Role of Political Parties, Civil Society and NGOs 3.4.1The Impact of the Colonial State 3.4.2The Impact of Social Exclusion 3.4.3The Impact of Democracy 3.4.4The Main Political Parties 3.4.4.1 The Umma Party 3.4.4.2 Democratic Unionist Party (DUP 3.4.4.3 Sudanese Communist Party (SCP 3.5 The Way Out 3.6 Conclusion CHAPTER FOUR travel expenses, when I was working with them as an accompanier for their North Sudan program.Alternatives, my Canadian former employer supported some o f my research expenses during my monitoring and field visits to their projects in Sudan.My family contributed greatly over the years, both financially and mentally.Some of the chapters were reviewed while I was visiting London, on my way to Swansea.Special thanks to Rose Muller and Tariq El-Ghadi, who provided pleasant accommodation and facilities for writing.I want to thank also Kitty Wamock for the early debate on NGOs
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".