Global Responses to the "War on Terror"
Bibliographic record
Abstract
The present collection seizes upon the momentum built by an emerging body of work that responds not only to the decade-long wars in Afghanistan and Iraq, but also to the multiple transnational reverberations of these conflicts: the realignment of geopolitical power relations; the formation of new terrorist networks (ISIS) and regional alliances (Iraq/Syria); the growing number of terrorist incidents in the West; the changing discourses on security and technologies of warfare; the leveraging of fundamental constitutional principles;and the ethical anxieties surrounding the lack of accountability for the violence carried out in the name of countering terrorism. The essays in this collection selectively reflect on these trajectories, which we have termed ‘global responses’ as they neither privilege one regional perspective over the other, nor define one discursive frame (‘War on Terror’) against another (‘9/11’). Instead, they concern themselves with the myriad representations of the political and cultural vicissitudes triggered by the responsive violence to 9/11 in select novels, poems, memoirs and films set in Iraq, Syria, Pakistan, Afghanistan, the Afghan–Pak border region, South Waziristan, Al-Andalus, Kenya, Canada, the US and the UK – works in which both the plots and the characters frequently pass through, at times surreptitiously, the Netherlands, Jordan, Senegal, Czechoslovakia, the Soviet Union and Mauritania.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".