Violent Obsessions: Esiaba Irobi’s Drama and the Discourse of Terrorism
Bibliographic record
Abstract
Quite apart from the revelation of the awesome might of the contemporary terrorist infrastructure and the vulnerability even of a superpower, the September 11, 2001, attacks on the United States have raised, with burning urgency, political, moral, ethical, and religious questions about terrorism. Crucial distinctions have been suggested between “terrorist organizations” and legitimate “liberation movements,” distinctions which only illustrate a conflict in definitions: ideological or religious persuasions as well as complex webs of allegiances and sympathies still play important roles in our categorizations of Osama bin Laden’s al-Qaeda, the (Real) IRA, the Basque Separatist Movement ETA, Hamas and Islamic Jihad, November 17, and others. And not only have the strategies of these organizations been recently reappraised, but some of the groups themselves have (at least theoretically) sought to modify their images by minimizing civilian causalities. Moreover, the U.S.-led onslaught against al- Qaeda and the Taliban in Afghanistan and the even more controversial liquidation of the Saddam Hussein regime in Iraq have also raised questions about whether some uses of violence are more morally objectionable than others.
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 | 0.002 |
| 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 | 0.015 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".