Capacity building for policy management through twinning: lessons from a Dutch–Namibian case
Bibliographic record
Abstract
Abstract Three previous articles of Public Administration and Development carried a debate on the contribution of twinning to capacity building in developing countries. Evidence was adduced to show that twinning could be a reliable vehicle for building and sustaining relevant capacity. On the other hand, some other sources contend that twinning is more of a metaphor than an actual strategy for building capacity. As an actual strategy it may be costly and unsustainable. These cases were part of Swedish, Norwegian and Canadian aid programmes. This article adds insights from a project which was instituted at the instance of the government of Namibia with a Dutch development institution. The focus is in‐country training in policy management for senior public officials and is complemented by off‐the‐job training and programmed visits by both northern and southern partners. The project has four 20‐month cycles and is currently into its second cycle as plans for the third cycle are being finalised. It is generally regarded as successful although this article takes a critical look at the potential of this project to fulfil its original mission of building two types of institutional capacities: high‐quality policy managers within the government and the capacity for policy management training at the country's only national university. It highlights the importance of demand‐drivenness, ownership and partnership, effective integration of theory with practice, mutual respect among partners without jeopardising quality. It also suggests possible strategies for tackling some of the emerging problems. Copyright © 2002 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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".