PENINGKATAN KAPASITAS MEDIA SOSIAL INTERPRETASI BUMI PERKEMAHAN PASIR BATANG DESA KARANGSARI KECAMATAN DARMA KABUPATEN KUNINGAN, JAWA BARAT, INDONESIA
Bibliographic record
Abstract
Kegiatan interpretasi bertujuan untuk menyampaikan berbagai hal terkait objek yang diinterpretasikan sehingga pengunjung dapat mengetahui, memahami dan ikut serta menjaga dan melestarikan objek. Kemajuan perkembangan teknologi informasi menghasilkan media yang semakin berkembang pula sehingga peluang penggunaan media untuk kegiatan interpretasi menjadi semakin beragam.Pelaksanaan kegiatan Peningkatan Kapasitas Media Sosial Interpretasi bagi Pengelola Bumi Perkemahan Pasir Batang dilakukan dengan beberapa tahapan, yaitu persiapan (pelatihan interpretasi), analisis potensi media sosial, desain media sosial, dan pengelolaan media sosial. Hasil kegiatan PkM ini adalah meningkatnya kapasitas anggota Kompepar dalam menginterpretasikan objek dan daya tarik wiasata melalui media sosial, dan media sosial yang terpilih adalah facebook.�Interpretation activities aim to convey various things related to objects that are interpreted so that visitors can know, understand and participate in maintaining and preserving objects. The progress of the development of information technology has resulted in increasingly growing media so that the opportunity for the use of media for interpretation activities becomes increasingly diverse. The implementation of Interpretation Media Capacity Building for the Manager of Pasir Batang Camping ground is carried out in several stages, namely preparation (interpretation training), analysis of the potential of social media, social media design, and social media management. The results of the PKM activities are the increasing capacity of Kompepar members in interpreting objects and the appeal of social facilities through social media, and the chosen social media is Facebook.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.045 | 0.013 |
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".