Bibliographic record
Abstract
BUKU kecil Dari McGill ke Oxford Bersama Ali Shari'ati dan Bint alShati' ini dirancang sebagai bab pertama dari buku Jihad Ilmiah Tiga: Dari Oxford ke Oxford. Buku Dari Oxford ke Oxford, seperti yang sudah saya tegaskan dalam Dari Harvard ke Yale dan Princeton, akan merekam jihad ilmiah saya menerbitkan tulisan di jurnal internasional. Namun demikian, perjalanan waktu menghendaki lain: bab pertama ini harus ditulis menjadi buku tersendiri dikarenakan alasan tertentu. Pertama, semula Dari McGill ke Oxford direncanakan hanya sekitar 10 (sepuluh) halaman. Setelah ditulis, ternyata, mulur: menjadi sekian panjang untuk sebuah bab, sehingga lebih baik dijadikan buku tersendiri. Di sini saya mendapat ilham: sebaiknya bab-bab selanjutnya juga saya kembangkan menjadi buku-buku tersendiri. Dengan demikian, isinya juga semakin banyak karena semakin tebal.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".