PENINGKATAN KOMPETENSI SUMBER DAYA MANUSIA DALAM PENGEMBANGAN SEKTOR PARIWISATA
Bibliographic record
Abstract
Salah satu aspek penting dalam mengembangkan pertumbuhan sector pariwisata adalahketersediaan aparatur. Pemerintah Kabupaten Sintang dalam hal ini Dinas Pemuda, Olah Raga danPariwisata menghadapi kendala minimnya ketersediaan apataur yang mempunyai kemampuan yangmemadai dalam mengembangkan potensi kepariwisataan yang ada. Rancangan penelititian yangdigunakan adalah penelitian deskriftif kualitatif, Sumber data dalam penelitian ini adalah Kepala DinasPemuda, Olah Raga dan Pariwisata Kabupaten Sintang, Kepala Bidang Pariwisata Dinas Pemuda,Olah Raga dan Pariwisata Kabupaten Sintang, Aparatur di Dinas Pemuda, Olah Raga dan PariwisataKabupaten Sintang. Dilihat dari aspek kompetensi pimpinan diketahui bahwa kemampuan pemikiranstrategis, kemampuan menghadapi berbagai perubahan yang terjadi serta kemampuan manajemenhubungan yang dimiliki belum mempunyai motivasi yang tinggi untuk memenangkan persaingan melaluipenemuan jasa-jasa, produk dan proses produksi yang baru. Upaya peningkatan kompetensi aparaturseperti pendidikan dan latihan yang berhubungan dengan sector kepariwisataan sudah cukup baikwalaupun masih belum optimal dilakukan hal ini disebabkan dampak dari pamdemi Covid-19. Faktorinternal yang mempengaruhi peningkatan kompetensi aparatur, yaitu ketersediaan dana, sikap pimpinan,motivasi pegawai serta ketersediaan sarana dan prasarana pendukung.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.007 |
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".