Bibliographic record
Abstract
Dalam analisis cluster mempelajari hubungan interdependensi antara seluruh set variabel perlu diteliti. Tujuan utama analisis cluster adalah mengekelompokkan obyek (elemen) seperti orang, produk (barang), toko, perusahaan ke dalam kelompok-kelompok yang relatif homogen berdasarkan pada suatu set variabel yang dipertimbangkan untuk diteliti. Obyek di dalam setiap kelompok harus relatif mirip/sama. Variabel-variabel pada cluster ini harus jauh berbeda dengan obyek dari cluster lain.Jika digunakan cara seperti ini maka analisis cluster merupakan bagian depan dari analisis faktor, dimana mereduksi (memperkecil) banyaknya obyek (responden) bukan banyaknya variabel atau atribut responden, yaitu mengelompokkan obyek-obyek tersebut kedalam cluster yang banyaknya lebih sedikit dari banyaknya obyek asli yang diteliti, misalnya dari 50 orang responden, dikelompokkan dengan 5 cluster dengan setiap cluster terdiri dari 10 orang. \n \nKata kunci : Cluster, mereduksi, interdependensi dan relatif sama
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".