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Record W4409801970 · doi:10.1080/13527258.2025.2496881

The afterlives of repatriation: heritage, emancipation and violence in Hindu nationalist India

2025· article· en· W4409801970 on OpenAlexaboutno aff
Vera Lazzaretti

Bibliographic record

VenueInternational Journal of Heritage Studies · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicAnthropological Studies and Insights
Canadian institutionsnot available
Fundersnot available
KeywordsHinduismEmancipationRepatriationNationalismHindu nationalismPolitical scienceHistoryCultural heritageAncient historySociologyReligious studiesLawPoliticsPhilosophy

Abstract

fetched live from OpenAlex

In 2021, a statuette of Hindu goddess Annapurna was taken from a Canadian museum to India as an arguably restorative, if not emancipatory, and decolonial achievement for a postcolonial nation. The statuette was enshrined at the Kashi Vishvanath temple in Banaras (Varanasi), a few metres from the Gyanvapi mosque – a longstanding target of Hindu nationalist campaigns for the mukti (liberation or emancipation) of supposedly originally Hindu sites. This article brings together Annapurna and the Gyanvapi mosque as two sides of the same story about heritage, emancipation and violence in Hindu nationalist India, and proposes an alternative methodological approach to the under-explored afterlives of repatriation. By combining longitudinal ethnographic research in the neighbourhood where Annapurna was enshrined with analysis of media and legal discourses, it teases out under-explored understandings that returned objects and repatriation itself afford in their post-repatriation locality – both in local responses and broader discussions around heritage restitution. I argue that repatriation cases such as that of Annapurna feed into a Hindu nationalist discursive ecology in which notions of emancipation, decolonisation and restitution are mobilised for majoritarian agendas: as exemplified by controversies around the Gyanvapi mosque, these notions increasingly underpin violent claims against minorities and their heritage.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.419
Threshold uncertainty score0.266

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.024
GPT teacher head0.388
Teacher spread0.364 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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