Community Participation In Forest Management In North Bandung
Bibliographic record
Abstract
Bahruzin. (2014). Analisis Efektivitas Kelembagaan Pengelolaan Hutan Bersama Masyarakat (PHBM) di Kesatuan Pemangkuan Hutan (KPH) Bandung Utara Jawa Barat. Bogor: Program Pascasarjana Institut Pertanian Bogor. Mulyana, Deddy. (2002). Metodologi Penelitian Kualitatif. Bandung : PT Remaja. Rosdakarya. Perum Perhutani. (2001). Pedoman Pengelolaan Sumberdaya Hutan Bersama Masyarakat. Online. http://arupa.or.id/sources/uploads/2012/04/2-SK-Dewan-Pengawas-No.-136-2001-PHBM.pdf Perum Perhutani. (2007). Pedoman Pengelolaan Sumberdaya Hutan Bersama Masyarakat. Online. http://arupa.or.id/sources/uploads/2012/04/5-SK-Direksi-No.-268-tahun-2007-PHBM-Plus.pdf PP Menhut (2014). Online. http://ppkh.dephut.go.id/uploads/attach/P.16-Menhut-II-2014.pdf Salim, Agus. (2001). Teori dan Paradigma Penelitian Sosial. Yogyakarta: Tiara wacana Slamet, Margono. (1985). Pengantar Sosiologi. Jakarta: CV. Rajawali Suparmoko. (1997). Ekonomi Sumberdaya Alam dan Lingkungan. Yogyakarta: BPFE-YOGYAKARTA. Udiansyah. (2009). Perencanaan Hutan adalah Tiang Pengelolaan Hutan Berkelanjutan. Dalam : Soendjoto MA dan Kurnain A, editor. Pengelolaan Sumber Daya Alam dalam Perspektif Kesejahteraan dan Keberlanjutan. Banjarmasin: Universitas Lambung Mangkurat Press. Uphoff, Norman. (1985). Participatory Evaluation of Participatory Development: a Scheme for Measuring and Monitoring Local Capacity. Newyork: Cornell University
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".