WORKSHOP PENGUATAN MANAJEMEN MEDIA DIGITAL DI DESA WISATA SIDOWAYAH, KLATEN, JAWA TENGAH
Bibliographic record
Abstract
Desa Wisata saat ini menjadi salah satu tren pariwisata di Tanah Air. Dengan memanfaatkan potensi lokal, banyak desa berlomba mempercantik diri agar bisa menjadi destinasi wisata. Hadirnya beragam teknologi digital utamanya internet dan media sosial menjadikan pariwisata desa-desa wisata di Tanah Air kian semarak. Namun, pandemi COVID-19 menjadikan banyak desa wisata di Indonesia ikut terdampak akibat penurunan jumlah wisatawan, salah satunya Desa Sidowayah, Kecamatan Polanharjo, Kabupaten Klaten, Jawa Tengah. Terkait dengan itu, untuk meningkatkan kembali kekuatan citra (branding) Desa Sidowayah sebagai destinasi wisata, maka Research Group D3 Komunikasi Terapan Sekolah Vokasi Universitas Sebelas Maret (UNS) Surakarta menyelenggarakan workshop penguatan manajemen media digital desa wisata. Kegiatan ini diselenggarakan pada Selasa, 5 Juli 2022 di Pendopo Umbul Siblarak, Desa Sidowayah, Klaten. Kegiatan dihadiri sekitar 20 peserta dari kalangan badan usaha milik desa, kelompok sadar wisata, pengelola media digital desa dan karang taruna. Materi kegiatan berupa penyegaran prinsip-prinsip manajemen profesional dalam tata kelola media digital desa wisata secara profesional meliputi perencanaan, pengorganisasian, pelaksanaan dan pengevaluasian. Selain itu, disampaikan tentang strategi teknis pembuatan media konten untuk berbagai platform media sosial.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.209 | 0.067 |
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".