Enhancing the role of International NGOs in promoting the implementation of ecosystem-based adaptation policies: insights from an International Union for Conservation and Foundation of Netherlands Volunteers led project in the Vietnamese Mekong Delta
Bibliographic record
Abstract
Several international non-governmental organizations (INGOs) function as boundary organizations and try to promote ecosystem-based adaptation (EbA) as a pivotal climate change adaptation strategy for coastal areas. This is being done in Vietnam. Few studies, however, have investigated how these INGOs operate, what challenges they face, and what conditions support them to successfully promote the implementation of EbA. To address this knowledge gap, the literature on international boundary work and boundary organizations was first reviewed, deriving four categories of conditions for a successful promotion of EbA: knowledge, networks, resources, and context. Next, we applied this framework in a case study of the International Union for Conservation (IUCN) and the Foundation of Netherlands Volunteers (SNV) led EbA project on the restoration and sustainable use of mangroves in two provinces, Ca Mau and Ben Tre, which are located in the Vietnamese Mekong Delta. We interviewed 25 key informants representing INGOs, Vietnamese governmental agencies, farmers, scientists, and market parties. Our case study not only revealed how most literature-based success conditions were met but also found some additional conditions. We found that INGOs will have a greater chance of successfully promoting the implementation of the EbA in cases in which they can act as a knowledge broker, have a strong international network, can supply enough resources, and use context-specific strategies. A supportive context appeared to be essential.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.007 | 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".