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Record W2738977963 · doi:10.1080/20549547.2017.1353317

Empire and the Reordering of Edibility: Deconstructing Betel Quid Through Metropolitan Discourses of Intoxication

2017· article· en· W2738977963 on OpenAlexfundno aff
Jaclyn Rohel

Bibliographic record

VenueGlobal Food History · 2017
Typearticle
Languageen
FieldDentistry
TopicOral Health Pathology and Treatment
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsContext (archaeology)Metropolitan areaEmpireColonialismHistoryGeographyAncient historyArchaeology

Abstract

fetched live from OpenAlex

Exotic, plant-based stimulants such as sugar, tea, and coffee linked metropolitan bodies with colonial expansion in support of the formation of modern Europe. But, not all tropical commodities easily flowed along these metropolitan routes. Betel quid, a prepared comestible, was among the most widely consumed stimulants in the world, but it was not popularized as a masticatory in modern Europe. This article traces the reconfiguration of betel quid in the British metropole in the early modern and modern periods. Through discursive analysis of medical and herbal texts, advertisements, British guides, and parliamentary papers, it shows how evolving discourses of intoxication motivated the betel quid’s transition from stimulating fruit into a largely inedible substance. Shifting notions of intoxication – first as a form of dissolute inebriation, and later as a state of measurable toxicity – provided the tools, language and social context to dissolve the betel quid’s edibility in conjunction with a broader civilizing process in imperial Britain. These metropolitan discourses reconfigured betel quid and in the process mobilized it as a new site of colonial differentiation.

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)0.004
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.989
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0110.068
Scholarly communication0.0080.008
Open science0.0010.007
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.049
GPT teacher head0.356
Teacher spread0.307 · 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.

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

Citations4
Published2017
Admission routes1
Has abstractyes

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