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Record W2808181577 · doi:10.17613/8c4jn-qv771

Kashmir as Movement and Multitude

2018· article· en· W2808181577 on OpenAlexaff
Omer Aijazi

Bibliographic record

VenueBrunel University Research Archive (BURA) (Brunel University London) · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicSouth Asian Studies and Conflicts
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMultitudeMovement (music)GeographyPolitical scienceAestheticsPhilosophyLaw

Abstract

fetched live from OpenAlex

The Line of Control arbitrarily bifurcates Neelum valley, Kashmir into Pakistan and India. While the border attempts to constrain and categorize, the daily movements and flows of human and more-than-human bodies via "unofficial" routes and routines generate an understanding of Kashmir that is not dependent on geopolitics. Neelum as sculpted and carved by the masculine gaze such as those of the nation-state and humanitarians - indicates closure. But the intrusion of interconnected bodies through the valley's vast landscapes suggest a continuous re-working and re-opening of its borders. These mobilities are stitched in the material inconveniences and intimacies of daily life in the valley. They are sustained by affective entanglements between human and more-than-human bodies constituting mutual processes of emplacement that are paradoxically unbounded and generative. In these movements and flows are analytical and philological opportunities to articulate fully formed visions of Kashmir. But this necessitates the location of theory and methodology as mutually constitutive within our literary genres (not outside of them) to elaborate narrative writing as praxis.

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)0.002
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0180.030
Scholarly communication0.0100.005
Open science0.0010.007
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.042
GPT teacher head0.313
Teacher spread0.271 · 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

Citations3
Published2018
Admission routes1
Has abstractyes

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