Harun al‐Rashid and the Terrorists: Identity Concealed, Identity Revealed
Bibliographic record
Abstract
The assumption of false identities is a frequent theme in history, fiction, and current events. Spies and criminals are among those who pretend to be other than they are, although the strategy is not restricted to them. Harun al‐Rashid, medieval Caliph of Baghdad, was described in the Thousand and One Nights as disguising himself in order to detect and punish evildoers. One distinctive feature of his adventures is that at some point he threw off the disguise and revealed his true identity. This paper recounts similar self‐exposures by spies and terrorists (including those of 9/11) in situations where such an act could spell disaster for them. It further explores a number of explanations for the “Harun al‐Rashid motive,” suggests a way to measure it, and discusses ways in which counterterrorism agencies could build upon it for their own purposes.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.040 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".