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Record W7135027124 · doi:10.1353/vcr.2025.a985004

Energy, Empire, and Colonial Assam's Tea Plantation Economy

2025· article· en· W7135027124 on OpenAlexvenueno aff
Chandrica Barua

Bibliographic record

VenueVictorian review · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCulinary Culture and Tourism
Canadian institutionsnot available
Fundersnot available
KeywordsColonialismIdeologyFlourishingRhetorical questionContext (archaeology)Chinese teaGovernment (linguistics)

Abstract

fetched live from OpenAlex

Abstract: This article examines the rhetorical function of "energy" in the late nineteenth- and early twentieth-century literary archive of the imperial tea industry in colonial Assam. After the "discovery" of tea in Assam in the early nineteenth century, the British colonial government commenced tea production in the region in newly established plantations. By the late nineteenth century, the imperial tea industry was a flourishing business, created and sustained by, as tea planter and self-proclaimed tea expert Samuel Baildon puts it, "British energy and enterprise." This was also the period through which energy science became a dominant field of intelligibility in the Victorian cultural imagination, and its innovations found many uses in the developing tea plantation sites. It is fair to surmise that most usages of the term would thus be affixed to Victorian energy physics discourses, especially in the context of the plantation as an industrial/technological site of operations. However, in the tea industry's promotional rhetoric, "energy" instead operated as a flexible, floating concept stretched across its pre-technological and its post-industrialization semantic histories. Reading the varied usage of "energy" in tea advertising, trade manuals, and tea planters' memoirs, I show how the elasticity of the term and the concept served to authorize and make legible the tea industry's ideological claims about morality, race, capital, and imperial futures.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.707
Threshold uncertainty score0.228

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 studies0.0000.000
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 head0.012
GPT teacher head0.240
Teacher spread0.229 · 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 designNot applicable
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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