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Record W2006499385 · doi:10.1080/08865655.2014.892695

The Borders that Divide, the Borders that Unite: (Re)interpreting Garo Processes of Identification in India and Bangladesh

2014· article· en· W2006499385 on OpenAlexvenueno aff
Ellen Bal, Timour Claquin Chambugong

Bibliographic record

VenueJournal of Borderlands Studies · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicSouth Asian Studies and Conflicts
Canadian institutionsnot available
Fundersnot available
KeywordsAssertionIdentification (biology)State (computer science)Ethnic groupUnificationColonial ruleColonialismGeographyDisconnectionPolitical scienceSociologyGender studiesLawArchaeology

Abstract

fetched live from OpenAlex

The people known as Garos, from the Garo Hills and adjacent (lowland) areas in India and Bangladesh, have never constituted one unified and self-defined in-group, although British colonial rule indeed produced a feeble notion of an imagined Garo community. Hence, the international border of 1947 formalized certain distinctions between hill Garos and lowlanders that had existed much longer, and gave a further impetus to the articulations of ethnic identities in different spaces. In recent years, however, we do see different attempts by the Garos to establish linkages across the border. This paper examines these processes of disconnection, exemplified by and through the international border, of unification (within the nation-state), and of (re)connection (across the border). We also try to show how the different strategies of the Indian and Pakistani/Bangladeshi states, in dealing with the populations in their borderlands, have impacted local processes of self-identification and self-assertion in significantly different ways, but with similar outcomes.

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)0.005
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 score0.026
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0080.028
Scholarly communication0.0080.006
Open science0.0010.007
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.030
GPT teacher head0.332
Teacher spread0.302 · 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

Citations14
Published2014
Admission routes1
Has abstractyes

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