Decentralised Cooperation and Local Government: Addressing Contemporary Global Challenges
Bibliographic record
Abstract
At the start of the last decade, United Cities and Local Governments’ (UCLG) policy paper on Decentralised Cooperation and Local Government laid out a clear rationale for decentralised cooperation and set out recommendations to the prevalent tackle weaknesses of international development cooperation and to strengthen development effectiveness. In many ways, the paper was a forerunner in calling for stronger sustained support for South-South development cooperation particularly among countries that have undergone similar socio-economic challenges so that learnings can be shared across partners. It laid emphasis on professional structures and programme-based approaches, with clear monitoring and evaluation tools and indicators on impact and called for a sharing of objectives across local and regional governments, and their associations, committed to continuing improvement, learning and exchange. These recommendations have helped strengthen international decentralised cooperation over the past decade, and their core principles continue to be highly relevant today. In 2021, the Institute of Development Studies, UK, with support of the UCLG Capacity and Institution Building Working Group (CIB), has engaged a wide range of member governments, associations, and networks, alongside a range of external commentators and experts, to assess UCLG principles, priorities, and actions in the context of contemporary global challenges and the resulting landscape of decentralised development cooperation. Following a series of survey-based, individual, case study, and workshop interactions, the study presents key points and recommendations.
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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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.003 |
| 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".