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Record W1912973008 · doi:10.1002/bsl.2118

Terrorism in Pakistan: A Behavioral Sciences Perspective

2014· review· en· W1912973008 on OpenAlexaff
Asad Tamizuddin Nizami, Mowadat Hussain Rana

Bibliographic record

VenueBehavioral Sciences & the Law · 2014
Typereview
Languageen
FieldSocial Sciences
TopicTerrorism, Counterterrorism, and Political Violence
Canadian institutionsQueen's University
Fundersnot available
KeywordsTerrorismPerspective (graphical)GeopoliticsDivergence (linguistics)PoliticsPolitical scienceCriminologyBehavioural sciencesSociologyPositive economicsPsychologySocial psychologyPolitical economySocial scienceLawEconomics

Abstract

fetched live from OpenAlex

This article reviews the behavioral science perspectives of terrorism in Pakistan. It can be argued that Pakistan has gained worldwide attention for "terrorism" and its role in the "war against terrorism". The region is well placed geopolitically for economic successes but has been plagued by terrorism in various shapes and forms. A behavioral sciences perspective of terrorism is an attempt to explain it in this part of the world as a complex interplay of historical, geopolitical, anthropological and psychosocial factors and forces. Drawing from theories by Western scholars to explain the behavioral and cognitive underpinnings of a terrorist mind, the authors highlight the peculiarities of similar operatives at individual and group levels. Thorny issues related to the ethical and human right dimensions of the topic are visited from the unique perspective of a society challenged by schisms and divergence of opinions at individual, family, and community levels. The authors have attempted to minimize the political descriptions, although this cannot be avoided entirely, because of the nature of terrorism.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.005
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.173
GPT teacher head0.528
Teacher spread0.355 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations8
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueBehavioral Sciences & the LawSame topicTerrorism, Counterterrorism, and Political ViolenceFrench-language works237,207