Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Introduction, Stephen Law (Heythrop College, UK) 1. Terrorisms in Palestine - Ted Honderich (UCL, UK) 2. Terror, Tomis Kapitan (University of Northern Illinois, USA) 3. The Morality of Palestinian Terrorism, Timothy Shanahan (Loyola Marymont University, USA) 4. Killing the Innocent, Richard Norman (University of Kent, UK) 5. Terrorism in the Israeli-Palestinian Conflict, Igor Primoratz (University of Melbourne, Australia) 6. Terrorism and Justice: Some Useful Truisms, Noam Chomsky (MIT, USA) 7. Terror in Palestine: A Non-Violent Alternative?, Stephen Law (Heythrop College, UK) 8. Casting the First Stone: Who Can, and Who Can't, Condemn the Terrorists, Gerald Cohen (University of Oxford, UK) 9. Murder and Morality: Professor Honderich on Israel and the Palestinians, Ardon Lyon (City University, UK) 10. Terror and Expected Collateral Damage: The Case for Moral Equivalence, Michael Neumann (Trent University, Canada) 11. In a World of Uneasy Virtue, William L. McBride (Purdue University, USA) 12. Talk and Terror: The Value of Just-War Arguments in the Context of Terror, Patrick Riordan (Heythrop College, UK) 13. Territory and Terrorism in Israel, Tamar Meisels (Tel-Aviv University, Israel) 14. Cosmopolitanism in a Time of Terror, Sharon Anderson-Gold (Rensselaer Polytechnic Institute, USA) 15. Tricks of Memory: Auschwitz and the Question of Palestiniam Terrorism, Brian Klug (University of Oxford, UK) Postscript, Ted Honderich (UCL, UK).
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.
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 | 0.001 | 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) | 0.000 | 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 it