ENGO Participation in Climate Change Governance: The Malaysian Experience from the Perspective of ENGOs
Bibliographic record
Abstract
Climate change issues are posing a threat to the world’s sustainable development. Geographically, Malaysia is located in a highly vulnerable region to the impacts of climate change, Southeast Asia. Thus, climate change governance based on stakeholder participation is needed to tackle climate change effectively. As non-state stakeholders, environmental non-governmental organisations (ENGOs) have been recognised as key actors in climate governance. In Malaysia, it has been observed that ENGOs have participated in several national committees to address climate change. However, there is limited empirical evidence demonstrating the current state of ENGO participation in Malaysia’s climate change governance. This paper presents a qualitative analysis to explore ENGO participation in Malaysian climate change governance, specifically focusing on the governance activities, level of participation and challenges for ENGO participation. The results reveal that ENGOs in Malaysia have participated in different phases of climate change governance activities, and it has been increasingly visible since the 1990s. While there have been improvements in the level of ENGO participation, the Malaysian government remains the dominant decision-maker in climate change governance. The ENGO participation in governance activities did not guarantee that their inputs were included in the final decisions. The study suggests that enhancing ENGO capacity-building, addressing key public officials’ receptivity towards ENGOs, and increasing partnerships with ENGOs, are vital to improving ENGO participation in Malaysian climate change 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".