Political Interaction Strategy Corporate Social Responsibility of PT Riau Andalan Pulp and Paper in Riau Province, Indonesia
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 research aims to analyze the corporate social responsibility (CSR) political interaction strategy used by PT Riau Andalan Pulp and Paper (RAPP) in Riau Province, Indonesia. This is qualitative research with data collected through interviews and the annual reports on the stock exchange of Indonesian companies. This research using cluster analysis to investigate the degree of correlation between political actors and companies. Furthermore, the N Vivo 12 Software was used to strengthen the actors built in the CSR political interaction strategy. The result showed that the constituency development strategy is the strongest of the four other political interactions. A constituency-building strategy means that companies seek to build relationships with multiple stakeholders in addressing political issues. The company's political interaction strategy for constituency development is building relationships with the community, policymakers, and Key Opinion Leaders while engaging with the media. It was also found that of four constituency development strategies in this company, the interaction with the media was the strongest relationship built to support political interaction and CSR.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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