Exploring the Roles of Environmental Non-Governmental Organisations in the Context of Malaysian Climate Change Governance
Bibliographic record
Abstract
As non-state actors, environmental non-governmental organisations (ENGOs) are able to influence the governance process in many cases. Although the ENGOs role in climate change governance has been extensively studied over the last two decades, there is a dearth of research relating to Malaysian ENGOs. Accordingly, this study was conducted to compensate for this gap in the literature, and it employs a qualitative approach via analysis of relevant documents and in-depth interviews with eleven ENGO informants operating in Malaysia at national, sub-national and local levels. The informants were selected through purposive sampling, and the interview data were analysed using thematic analysis. The informants described the ENGO roles in climate change governance at national, sub-national, and local levels, and their roles were described in the context of the following six key themes: political, informational, educational, complementary, observational, and innovational. Similar to earlier perspectives, the results of the present study revealed that the government generally accepts the ENGOs’ role to facilitate climate change governance. This article provides insight into the ENGO’s role in assisting the government in governing climate change in Malaysia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".