Management standards and development practice in the South African aid chain
Bibliographic record
Abstract
Abstract The article addresses how South African non‐governmental organisations (NGOs) approach the management of their development activities and the influences upon their approaches. Based on interviews, field visits and programme documents from 40 organisations working in South Africa, the article explores the extent to which NGO programme priorities and adopted management practices arise out of donor conditions, succeed in their stated aims and generate other unintended consequences. Four aspects of contemporary NGO management dynamics are explored: logical frameworks, participatory processes, impact enhancement and financial probity. While donor requirements in these four areas generally impose heavy costs on South African NGOs and poorly achieve their stated aims, the research documents cases, in which local managers were able to work effectively and learn within these constraints, found ways around the more intrusive requirements, or challenged donors to change their policies to permit more equitable donor‐recipient relationships and better development practice. However, an unintended impact of tighter funding requirements is an observable differentiation within the South African NGO sector, with smaller community‐based organisations excluded as larger professional organisations establish more enduring links with international development organisations. Copyright © 2003 John Wiley & Sons, Ltd.
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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 | 0.028 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.016 | 0.018 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".