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Record W2156090493 · doi:10.1080/08865655.2014.892694

The India–Bangladesh Border Fence: Narratives and Political Possibilities

2014· article· en· W2156090493 on OpenAlexvenueno aff
Duncan McDuie‐Ra

Bibliographic record

VenueJournal of Borderlands Studies · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicSouth Asian Studies and Conflicts
Canadian institutionsnot available
Fundersnot available
KeywordsFence (mathematics)NarrativePoliticsSovereigntyPolitical scienceSociologyPolitical economyLawEngineering

Abstract

fetched live from OpenAlex

The fencing of the India–Bangladesh border mirrors Scott's understanding of “final enclosure” wherein “distance-demolishing technologies” and “modern conceptions of sovereignty” converge to demarcate firm boundaries of territory from previously ambiguous space (Scott, J. 2009. The Art of Not Being Governed: An Anarchist History of Southeast Asia, 11. New Haven: Yale University Press). This paper examines the different narratives surrounding the fence at the national level in India and in the borderland itself, focussing on the state of Meghalaya. These narratives reveal the ways the border fence is discussed and understood and the political positions taken on the fence in these different spaces. In examining these I present two key findings. The first is that the border fence is narrated and politicized differently at the national level and in the borderland. The second is that within the borderlands there is not a singular “borderland narrative” of the fence but several, reflecting dominant political positions already entrenched and new ways of articulating insecurity being brought by fence construction; though the former is more prominent than the latter.

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)0.003
metaresearch head score (Gemma)0.003
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0170.023
Scholarly communication0.0090.007
Open science0.0010.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.022
GPT teacher head0.350
Teacher spread0.328 · 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

Citations33
Published2014
Admission routes1
Has abstractyes

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