Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".