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 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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.122 | 0.054 |
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".