Ethno-Territorialized Bodies: Necropolitics in Pakistan’s Tribal Areas and Non-violent Organic Resistance
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.041 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".