Komunikasi Persuasif pada Program Community CleanUp dalam Membangun Memelihara Kebersihan Lingkungan
Bibliographic record
Abstract
Abstract. Environmental pollution due to waste is still a problem that has not yet received a maximum solution in the city of Bandung. In addition to the role of the government which functions to provide waste disposal facilities, it also requires the role of the community to manage waste independently. Therefore, public awareness about environmental cleanliness is very important to do, one of which is done by the NGO River Clean Up Indonesia in the Community Cleanup program inviting people to maintain environmental cleanliness. The purpose of this research is to find out how the persuasion communication of storytelling, persuasion communication techniques, obstacles in persuasion communication activities and efforts to overcome them, and inclusive leadership as flagship. The theory used in this research is Symbolic Interactionism theory. The method used in this research is qualitative with a case study approach. The data collection techniques were observation, interview, and documentation. The subjects in this research are the Project Lead, Members and Volunteers of River Clean Up Indonesia. Abstrak. Pencemaran lingkungan akibat sampah masih menjadi masalah yang belum mendapatkan solusi maksimal di kota Bandung. Selain peran pemerintah yang berfungsi menyediakan fasilitas pembuangan sampah, dibutuhkan juga peran masyarakat untuk pengelolaan sampah secara mandiri. Maka dari itu, kepedulian masyarakat mengenai kebersihan lingkungan sangat penting untuk dilakukan, salah satunya seperti yang dilakukan oleh NGO River Clean Up Indonesia dalam program Community Cleanup mengajak masyarakat untuk memelihara kebersihan lingkungan. Tujuan penelitian ini adalah untuk mengetahui bagaimana komunikasi persuasi storytelling, teknik komunikasi persuasi, hambatan dalam kegiatan komunikasi persuasi dan upaya mengatasinya, serta kepemimpinan inklusif sebagai unggulan. Teori yang dimanfaatkan pada penelitian ini adalah teori Interaksionisme Simbolis. Metode yang digunakan pada penelitian ini adalah kualitatif dengan pendekatan studi kasus. Teknik pengumpulan data dilakukan dengan observasi, wawancara, dan dokumentasi. Subjek pada penelitian ini adalah Project Lead, Anggota dan Relawan River Clean Up Indonesia.
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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 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".