Toward adaptive water governance: the role of systemic feedbacks for learning and adaptation in the eastern transboundary rivers of South Africa
Bibliographic record
Abstract
This paper contributes to scholarship on adaptive water governance (AWG), following policy reforms in South Africa, through a focus on systemic feedbacks for learning and adaptation as critical aspects of AWG. We draw insights from three innovative and evolving water governance experiments. In 1998 South Africa adopted integrated water resources management (IWRM) as a transformative approach for achieving an equitable, sustainable, and decentralized stakeholder-centered water resources governance: all hallmarks of an enabling environment for a two-decade history of AWG, although not named as such. Progress in AWG is explored by using a longitudinal, evaluative exploration of three cases in two transboundary basins in South Africa, with a focus on the unfolding enabling environment for achieving sustainability and equity. Building on previous work, we present and discuss a range of enablers that are shown to function systemically to support feedbacks and build adaptive capacity and resilience in complex and uncertain river systems. In the Crocodile Basin, meta-governance arrangements that created an enabling space for collaborative experimentation and learning proved critical as feedbacks were progressively strengthened and embedded through evolving social and institutional arrangements. The enabling environment also supported a networked, blended system of stakeholder- and state-led platforms that have co-evolved through experimentation and learning. Despite progress, long-term persistence of action-learning feedbacks appears less certain in the Olifants Basin cases. We suggest that enabling meta-governance arrangements that offer an institutional home within which to embed learning is critical. The need to explore alternative networked governance arrangements and to explicitly manage for feedbacks that enhance learning at multiple scales is emphasized. We conclude with recommendations for future work on AWG, including reconciling differences between AWG and IWRM that originally framed South African reforms.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".