Bibliographic record
Abstract
In late 2013 I was sitting in Dinar Matahari, an airy cafeteria inside the headquarters of the Malaysian Central Bank (Bank Negara), with Anwar, a senior bank official responsible for regulating Islamic finance in the country. It was late afternoon and the cafeteria was sparsely occupied. Employees had already begun wiping down tables, signaling the end of another workday. Anwar and I had started our conversation by discussing the technical aspects of Islamic finance, focusing on Bank Negara's plans to develop standard forms for twelve contracts that it had identified as pivotal to the industry and discussing some of the problems around calculating risk in Islamic finance. 1 Abruptly, Anwar shifted gears and asked: "Is it OK to talk about religion?" He then launched into a monologue that, at first, seemed to have little to do with the preceding discussion. Only Allah and the prophet Muhammad, he explained, could know what would happen in the future: "We act as if we will live forever . . . but our behavior must be checked." The check on human behavior was
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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 teacher head, 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".