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Record W4381885053 · doi:10.1080/08865655.2023.2226402

Ethno-Territorialized Bodies: Necropolitics in Pakistan’s Tribal Areas and Non-violent Organic Resistance

2023· article· en· W4381885053 on OpenAlexvenueno aff
Azmat Khan, Faizullah Jan, Syed Irfan Ashraf

Bibliographic record

VenueJournal of Borderlands Studies · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Security and Public Health
Canadian institutionsnot available
Fundersnot available
KeywordsBiopowerGrassrootsResistance (ecology)State (computer science)IndigenousSpanish Civil WarPolitical scienceMandateSociologyGender studiesPolitical economyLawPolitics

Abstract

fetched live from OpenAlex

For the last five decades, a neoliberal war has been raging through Pakistan’s Pashtun Tribal Areas bordering Afghanistan. The State of Pakistan has been an active shareholder in this never-ending “war on terror.” The State’s biopolitical and racializing policies continuously reproduce the logics through which the Tribal bodies are conveniently and invisibly subjected to a regime of death and maiming. In an attempt to unsettle the State’s biopolitical mandate, we combine Michel Foucault’s biopolitics and Achille Mbembe’s necropolitics to map out the historical genealogy of the imperialist violence in the Tribal borderzone. Next, we delineate and examine three major State-deployed discursive strategies through which the exceptional position of this Tribal borderspace and the expendable status of its inhabitants are constructed and naturalized. Towards the end, we also discuss PTM, a grassroots civil rights anti-war movement which is mounting a notable resistance to the necropolitical regime through a non-violent praxis rooted in Pashtun cultural traditions of resistance. The PTM, we conclude, offers an organic counter Pashtun narrative to the world. The aim of the paper is to stimulate indigenous emancipatory perspectives in the academic literature on the Pashtun Tribal Areas and its people.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.408
Threshold uncertainty score0.963

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.035
GPT teacher head0.394
Teacher spread0.359 · 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.

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

Citations6
Published2023
Admission routes1
Has abstractyes

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