Optimizing Government Policy in Efforts to Restore Tourism Post Covid 19 Pandemic in Bukittinggi City
Bibliographic record
Abstract
Artikel ini membahas tentang optimalisasi kebijakan pemerintah dalam upaya pemulihan pariwisata pasca pandemi covid 19 di Kota Bukittinggi dan tetap menjadi tujuan destinasi utama para pengunjung wisatawan,. Penelitian ini menggunakan deskriptif kualitatif..Teknik pengumpulan data di dalam penelitian ini diperoleh dengan teknik wawancara, observasi dan mengumpulkan data-data sekunder. Data juga diperoleh dengan cara mengamati fenomena-fenomena di lapangan. Upaya Pemerintah Kota Bukittinnggi terhadap pariwisata dalam pemulihan pasca pandemi Covid-19 yang telah dilakukan: 1. Penerapan Kebijakan Pemerintah Kota Bukittinggi dalam Memutus Mata Rantai Penyebaran COVID-19 2. Kendala yang dihadapi adalah persoalan kepatuhan masyarakat Buktinggi yang belum sepenuhnya menyadari akan bahaya pandemi Covid-19. 3. Upaya Kebijakan Pemerintah Kota Bukittinggi Memajukan Sektor Pariwisata Pasca Pandemi Covid 19.Kebijakan yang perlu dibenahi setelah COVID-19 adalah masalah sarana pendukung parkir. Peningkatan sarana dan prasarana pendukung pariwisata Bukittinggi dan sarana prasarana di objek wisata. Melakukan kerjasama dengan instansi atau dinas terkait untuk mengembangkan Bukittinggi. Meningkatkan kenyamanan pengunjung dengan melakukan sosialisasi sapta pesona terhadap masyarakat. Meningkatkan keamanan dan citra Kota Bukittinggi yang bersih dan bebas dari praktek praktek PEKAT. Kata Kunci : Optimalisasi, Kebijakan Pemerintah, Pariwisata, Pasca Covid 19
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".