Public Participation in Environmental Impact Assessment (EIA) of the Thailand-Malaysia Gas Separation Plant Project
Bibliographic record
Abstract
This article aims to study the level of the public participation in the Environmental Impact Assessment (EIA) study of the Thailand-Malaysian gas seperation plant project; Chana district, Songkhla province. The study was found that the level of public participation is lower than the developed countries such as United State of America and Canada due to few limitations. Particularly, the project is a new occurrence in Thailand that has carried out the public participation system into the EIA study. Consequently, there were inadequate experience to encourage the public participation and to manage the conflicts between the opponent groups and the project owner. Therefore, the method of the solving problem is that public should participate at the beginning stage; to select the proper area for the project and proceed to the final stage; to take parts in the decision making process for decision of construction. Nevertheless, the latter time, government tried to hold the public hearing, however, is far from success. Consequently, there were many violent situations. In particularly, on December, 20, 2003, there was confrontation and clashed between policemen and opponents group. Furthermore, government should clearly set the public participation process in EIA study and to clearly state that who is the public such as, the affected people, interesting people, academic people, and NGO to participate into the EIA study.
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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".