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Record W4200154260 · doi:10.18357/bigr31202120263

Identity, Religion and Difference in the Borderland District of Poonch, Jammu and Kashmir

2021· article· en· W4200154260 on OpenAlexvenueno aff
Malvika Sharma

Bibliographic record

VenueBorders in Globalization Review · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicSouth Asian Studies and Conflicts
Canadian institutionsnot available
Fundersnot available
KeywordsEthnic groupCommunalismPartition (number theory)Gender studiesReligious identityPoliticsSociologyEthnographyIdentity (music)GeographyAnthropologySocial sciencePolitical scienceLawNegotiationAesthetics

Abstract

fetched live from OpenAlex

This article (part of a special section on South Asian border studies) is an exploration of a multi-religious ethnic group in the borderland district of Poonch in Jammu and Kashmir, India. The work focuses on the Pahari ethnicity and looks at how prominent religious identities within this group have been continuously aligning themselves along religious lines in the post-partition era. Partition of the Indian subcontinent in 1947 acted as a major disruption in the construction of identities. The evolution of national and ethnic identities went hand in hand with the evolution of religious identities, with the latter being more pronounced than the former. Such a fixation along religious lines in the socio-cultural and political sphere led to changes in everyday inter-community relations. Through oral histories and other accounts, this ethnography understands the new set of interactions that emerged in Poonch which have been shaping identities, while also analysing identity construction and its impact on the social organisation of space and neighbourhoods in general in the post-partition era. Key Words: Borderland, Border, Boundaries, Community, Communalism, Difference, Ethnicity, Identity, Inter-community interaction, Nation, Nation-State, Othering, Religion

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)0.001
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 score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0020.003
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.343
Teacher spread0.325 · 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
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueBorders in Globalization ReviewSame topicSouth Asian Studies and ConflictsFrench-language works237,207