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Record W4417174410 · doi:10.1080/08865655.2025.2599159

Crossing Lines: Kinship, Security, and Identity in Mizoram’s India–Myanmar Border Communities

2025· article· en· W4417174410 on OpenAlexvenueno aff
James Ralte, Vineeth Thomas

Bibliographic record

VenueJournal of Borderlands Studies · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicSouth Asian Studies and Conflicts
Canadian institutionsnot available
Fundersnot available
KeywordsIdentity (music)Border crossingIdentification (biology)Cultural identityImmigration

Abstract

fetched live from OpenAlex

This article examines how the India–Myanmar border in Mizoram is being reimagined and contested amid stateled securitization measures, with a focus on their impact on Zo communities whose familial, cultural, and religious ties span both sides. It asks how policy shifts, such as the 2024 suspension of the Free Movement Regime (FMR) and plans to fence the border, alter local notions of security, mobility, kinship, and belonging. Drawing on policy documents, media reports, and scholarly literature, the analysis situates these developments within a framework that views borders as socially produced and negotiated. The article defines core concepts of security (as territorial control and discourse), mobility (as everyday movement and exchange), kinship (as transborder family networks), and belonging (as identity and place in the borderland) and uses these to interpret how Mizoram's communities experience and respond to securitization. By examining state strategies and local responses, opposition by the Mizoram government and civil society, the study contributes to debates on postcolonial border governance, Indigenous rights, and federalism in South Asia. It argues that borders are experienced not only as imposed lines of separation but also as lived spaces of connection, where security imperatives collide with enduring kinship ties and local agency.

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)0.001
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.485
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
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 head0.037
GPT teacher head0.404
Teacher spread0.368 · 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 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

Citations1
Published2025
Admission routes1
Has abstractyes

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