The Effectiveness of Community Development and Environmental Protection Program in Oil and Gas Industry in Indonesia: Policy, Institutional, and Implementation Review
Bibliographic record
Abstract
In all industries, inclusively oil and gas, Corporate Social Responsibility (CSR), including community development and environmental protection, have a significance role for the sustainability of company business. However, despite established government regulations and a large amount of company budget for the program, the big question that arises is whether these programs have been effectively addressed the social and environmental needs of surrounding communities, and whether the program succeeded in supporting the sound operation of oil and gas companies. This qualitative research is conducted by collecting data from the central and local governments in Indonesia, from oil and gas companies throughout Indonesia, as well as from communities near operating areas, through desk study, survey, in-depth interviews, and Focus Group Discussion (FGD). The results showed that, in general, the community development and environmental protection program have been implemented. Unfortunately, the programs have not been yet fit to the societies socio-environmental condition, have not been yet answered all communities’ social, economic, environmental, and cultural issues, and have not been yet fully supported the sound operation of the company. This means that the programs have not been used effectively.
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 | 0.022 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".