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Record W1985284412 · doi:10.1017/s0026749x13000589

Secularizing the Sacred, Imagining the Nation-Space: The Himalaya in Bengali travelogues, 1856–1901

2014· article· en· W1985284412 on OpenAlexaff
Sandeep Banerjee, Subho Basu

Bibliographic record

VenueModern Asian Studies · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicPhilippine History and Culture
Canadian institutionsMcGill University
Fundersnot available
KeywordsHomelandBengaliHinduismCasteSpace (punctuation)VernacularPoliticsHistoryAnthropologyGender studiesReligious studiesSociologyLiteratureArtPhilosophyPolitical scienceLinguisticsLaw

Abstract

fetched live from OpenAlex

Abstract This article examines changing conceptions of the Himalaya in nineteenth-century Bengali travelogues from that of a sacred space to a spatial metaphor of a putative nation-space. It examines sections of Devendranath Tagore's autobiography, written around 1856–58, before discussing the travelogues of Jaladhar Sen and Ramananda Bharati from the closing years of the nineteenth century. The article argues that for Tagore the mountains are the ‘holy lands of Brahma’, while Sen and Bharati depict the Himalaya with a political slant and secularize the space of Hindu sacred geography. It contends that this process of secularization posits Hinduism as the civil religion of India. The article further argues that the later writers make a distinction between the idea of a ‘homeland’ and a ‘nation’. Unlike in Europe, where the ideas of homeland and nation overlap, these writers imagined the Indian nation-space as one that encompassed diverse ethno-linguistic homelands. It contends that the putative nation-space articulates the hegemony of the Anglo-vernacular middle classes, that is, English educated, upper caste, male Hindus where women, non-Hindus, and the labouring classes are marginalized.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.051
Threshold uncertainty score0.101

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0080.011
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.046
GPT teacher head0.306
Teacher spread0.260 · 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

Citations9
Published2014
Admission routes1
Has abstractyes

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