Perencanaan Konten Media Sosial Dalam Event Road Tour Legacy Of Java X Sidji Batik Coffee Series
Bibliographic record
Abstract
Tujuan dari penelitian ini adalah melakukan kegiatan perancangan konten media social dalam rangka mempromosikan event “Road Tour Legacy of Java Batik Sidji X Coffee Series”. Kegiatan perancangan ini dimulai dari pengumpulan data secara kualitatif untuk mengetahui perencanaan media promosi yang digunakan. Teknik Pengumpulan data dilakukan dengan melakukan wawancara dan observasi secara langsung kepada salah satu seniman yang terlibat dalam kegiatan event. Hasil penelitian menunjukan perancangan konten event dilakukan dengan Menentukan Platform, Menentukan konten, Analisis & Evaluasi SWOT (Strenghts, Weaknesess, Opportunities, dan Threats), dan Action & Communication.. Media sosial pembuatan konten yang sesuai seperti yang dibuat oleh Batik Sidji yaitu film pendek mengenai Batik pada Channel Youtube, dan informasi yang diberikan Batik Sidji melalui Instagram dapat menarik perhatian masyarakat terhadap kesenian Batik.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 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) | 0.095 | 0.021 |
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".