The Communities Understandings on the Roles of Government in Mitigating Trans boundary Haze Pollution in Sarawak Malaysia
Bibliographic record
Abstract
This study examines the communities understandings on the disaster risk management, government actions in the legislation and enforcement and the role of ASEAN’s in dealing with trans boundary haze pollution in the Sri Aman, Sarawak. Data collection methods was based on the interview schedule. The analysis was constructed on one hundred (100) respondents. A five-point Likert Scale is used which consist of five main constructs that us related to the objectives of the study namely, the community’s understanding of the haze issue, the awareness of the Disaster Risk Management on haze, government’s action in addressing the haze issue, ASEAN’s role in dealing with trans boundary haze pollution and the impact of haze on community’s livelihood. The study’s result shows that community in Sri Aman are generally agree with understanding on the haze issue with the mean of 4.39 and standard deviation of 0.81. Besides, the community are also aware on the Disaster Risk Management towards haze with the respondent’s feedback that shows that almost 70.0 percent agreed as their feedback. Majority of the respondents (93.86%) with the mean of ranging from 4.26 to 4.83 and standard deviation from 0.38 to 0.91 are agree and support the action from government. The construct on ASEAN’s role in dealing with trans boundary haze pollution has a positive result with mean of 4.20 and standard deviation of 0.77. While, the impact of haze towards their livelihood shows that most of the respondents understand with the value mean of 4.29. The communities in Sri Aman understand the Disaster Risk Management, a government’s action on the legislation and enforcement and the ASEAN’s role when dealing with trans boundary haze. However the community need to be exposed with Disaster Risk Management Training and adopt it is because it will help them to analyse and learn from their experience on the disaster. Eventually it will enhance the communities understanding on risk posed by trans boundary haze..
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".