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Record W6929691417 · doi:10.5167/uzh-197429

Global Responses to the "War on Terror"

2018· article· en· W6929691417 on OpenAlexaboutno aff

Bibliographic record

VenueZurich Open Repository and Archive (University of Zurich) · 2018
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicDrug-Induced Hepatotoxicity and Protection
Canadian institutionsnot available
Fundersnot available
KeywordsGeopoliticsTerrorismPoliticsPower (physics)Privilege (computing)MemoirAccountabilityPerspective (graphical)Foreign policy

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.435
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.088
GPT teacher head0.379
Teacher spread0.291 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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