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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.068 | 0.016 |
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".