Vertical integration for climate change adaptation in the water sector: lessons from decentralisation in Africa and India
Bibliographic record
Abstract
Abstract Vertical integration, which creates strategic linkages between national and sub-national levels, is being promoted as important for climate change adaptation. Decentralisation, which transfers authority and responsibility to lower levels of organisation, serves a similar purpose and has been in place for a number of decades. Based on four case studies in semi-arid regions in Africa and India, this paper argues that vertical integration for climate change adaptation should reflect on lessons from decentralisation related to governing natural resources, particularly in the water sector. The paper focuses on participation and flexibility, two central components of climate change adaptation, and considers how decentralisation has enhanced or undermined these. The findings suggest that vertical integration for adaptation will be strengthened if a number of lessons are considered, namely (i) actively seek equitable representation from marginal and diverse local groups drawing on both formal and informal participation structures, (ii) assess and address capacity deficits that undermine flexibility and adaptive responses, especially within lower levels of government, and (iii) use hybrid modes of governance that include government, intermediaries and diverse local actors through both formal and informal institutions to improve bottom-up engagement.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".