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Record W4414596744 · doi:10.1007/s44282-025-00242-0

Development-induced interventions and socio-cultural transitions among the tribes of North-East India

2025· article· en· W4414596744 on OpenAlexaff
Lal Chhandama, Koustab Majumdar, Arunava Sengupta

Bibliographic record

VenueDiscover Global Society · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicSouth Asian Studies and Conflicts
Canadian institutionsYork University
Fundersnot available
KeywordsIndigenousGeopoliticsPsychological interventionMulticulturalismPoliticsColonialism

Abstract

fetched live from OpenAlex

This article examines development-induced interventions and their socio-cultural impacts on tribal populations in Northeast India. It highlights the profound socio-cultural transitions, including cultural dilution and acculturation, which have been longstanding concerns for these communities. The analysis traces the roots of these transitions to British colonialism and its dominant policies, which fostered economic stratification, the spread of Western knowledge, and political administration. These policies facilitated the processes of cultural erosion and the loss of Indigenous identity. Furthermore, the article explores how geographical and geopolitical tensions, particularly those associated with military presence, cross-border interactions, and inadequate state policies, have exacerbated socio-cultural transitions and fuelled ethnopolitical movements. It also examines the role of technological advancements in driving socio-cultural shifts, acknowledging their mixed impact. While technology has contributed to economic and social progress, it has also led to displacement, loss of land and Indigenous rights, and cultural assimilation. One notable example is the influence of ‘Koreanisation,’ which fosters multiculturalism and challenges traditional cultural identities. In conclusion, the article underscores the importance of balancing development initiatives with preserving cultural heritage. Ensuring this equilibrium is crucial in addressing the needs of tribal communities, allowing for progress without undermining cultural integrity.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.150
Threshold uncertainty score0.938

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.027
GPT teacher head0.316
Teacher spread0.290 · 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 designObservational
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

Citations0
Published2025
Admission routes1
Has abstractyes

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