Is the ecosystem approach effective in transboundary water systems: Central Asia as a case study?
Bibliographic record
Abstract
Abstract In the context of international environmental law and International Water Law (IWL), the Ecosystem Approach (EA) has become a source of heated debate. In recent years, there has been growing recognition of the negative impacts that human activities have on freshwater ecosystems. Accordingly, the protection of such ecosystems has been identified as integral to ensuring the good governance of water resources. This article reviews key areas of research around the conceptualization and application of EA. First, we adopt a holistic approach to the concept of EA when applied to existing environmental challenges, before exploring the issues that arise when applying EA to water‐based ecosystems. Next, we assess the effectiveness of implementing EA in the management of environmental issues linked to transboundary water contexts. Our findings indicate that International Environmental Law, which applies a sector‐specific approach, poses challenges for the instrumental implementation of EA because the latter requires a holistic approach to resource management. Furthermore, in transboundary water contexts the competing needs of river‐basin countries are also identified as key factors complicating the implementation of EA. The article concludes with recommendations for policy makers and scholars. This article is categorized under: Water and Life > Conservation, Management, and Awareness Engineering Water > Planning Water Human Water (WBAA) > Water Governance
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".