Policy Strategy To Optimize Corporate Social And Environmental Responsibility Implementation In Buleleng Regency
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This policy paper discusses strategies for optimizing the implementation of Corporate Social and Environmental Responsibility (CSR) in Buleleng Regency, which has not yet been implemented effectively. Of the 1,421 registered companies, only 7.67 percent have implemented CSR. This situation is caused by weak coordination among stakeholders due to the lack of technical guidelines (juknis) and standard operating procedures (SOPs) for CSR implementation. The research used a qualitative descriptive approach through interviews, focus group discussions (FGDs), and document studies. The analysis was conducted with reference to institutional theory (Scott, 2001), stakeholder theory (Freeman, 1984), and the principles of good governance (UNDP, 1997). Three policy alternatives are proposed: (1) strengthening the CSR Forum through the development of articles of association (AD/ART), (2) developing and establishing CSR Technical Guidelines and Standard Operating Procedures (SOPs), and (3) developing a digital-based CSR information system. Based on the assessment using the Bardach (2012) method for the criteria of effectiveness, efficiency, political feasibility, sustainability and public acceptability, the second alternative obtained the highest score (23). This policy is considered the most strategic because it clarifies coordination mechanisms, increases legal certainty, and serves as a foundation for strengthening forums and digitizing the system in the future. Implementation of this policy is expected to achieve more transparent, measurable, and sustainable CSR governance, as well as strengthen synergy between local governments, businesses, and communities for inclusive and equitable development in Buleleng Regency.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it