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Record W1965170417 · doi:10.1093/afraf/adr029

Securing Africa: Post-9/11 discourses on terrorism

2011· article· en· W1965170417 on OpenAlexaffabout
Bruno Charbonneau

Bibliographic record

VenueAfrican Affairs · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicTerrorism, Counterterrorism, and Political Violence
Canadian institutionsLaurentian University
Fundersnot available
KeywordsTerrorismPolitical scienceMedia studiesSociologyLaw

Abstract

fetched live from OpenAlex

This book examines the various ways in which Africa has been implicated in the politics and practices of security since 11 September 2001. It focuses on the effects and outcomes of American policy and security discourses (from George W. Bush to Barack Obama) on the people and states of Africa by exploring the links between terrorism, the ‘war on terror’, democracy, human rights, and diverse security practices of the post-9/11 world order. One of the key objectives of the collection is to ‘foreground knowledge that is local, subaltern and from peripheral regions; that is discontinuous and disruptive to progressive notions of history; and that may even be characterized as “illegitimate” from the standpoint of conventional terrorism studies’ (p. xvi). The book begins with a substantial and provocative essay written by the editor in which she deconstructs and exposes the effects of ‘terrorism thinking’. For Smith, the production of knowledge associated with ‘terrorism’ is a central issue. The fact that ‘an event in a metropolitan center is established as world transformative’ can only be called into question when it is compared to alternative claims or narratives from the periphery (p. 17). An analysis of post-9/11 discourses on terrorism from marginalized perspectives is a strategy that ‘exposes the normative implications of the production of hegemonic voice and stories about political violence and terrorism’ (p. 17).

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0100.018
Scholarly communication0.0090.009
Open science0.0010.004
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0090.001

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.

Opus teacher head0.041
GPT teacher head0.283
Teacher spread0.243 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations13
Published2011
Admission routes2
Has abstractyes

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