REKRUTMEN PEMBINAAN PUSAT PENDIDIKAN DAN LATIHAN MAHASISWA ATLETIK DAN PENCAK SILAT FIK UNM
Bibliographic record
Abstract
Tujuan penelitian ini untuk mengungkap tentang bagaimana proses rekrutmen calon atlit PPLM FIK-UNM. Metode penelitian ini menggunakan Observasi terus terang, structured interview, dan dokumentasi. Teknik pengumpulan data menggunakan jenis triangulasi yang dilakukan hingga peneliti cukup dalam mendapatkan data yang diharapkan. Proses analisa data dilakukan sebelum dan selama penelitian berlangsung dengan menggunakan model spradley untuk menguji hipotesis penelitian ini. Hasil penelitian yang diperoleh adalah sistem perekrutan atlet PPLM yang dilakukan selama ini telah transparan dan sesuai dengan visi dan misi yang telah dirumuskan. Input yang dihasilkan diharapkan dapat menunjang tercapainya tujuan PPLM yakni meningkatkan prestasi olahraga dari kalangan intelektual dan secara umum untuk mencari bibit atlet yang berpotensi untuk berprestasi sebagai jawara Indonesia di dunia olahraga Internasional
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.034 | 0.009 |
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; both teacher heads agree on what is shown here.
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".