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Record W2739828741 · doi:10.1080/20549547.2017.1355651

The Delectable and Dangerous: Durian and the Odors of Empire in Southeast Asia

2017· article· en· W2739828741 on OpenAlexaff
Daniel E. Bender

Bibliographic record

VenueGlobal Food History · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Maritime and Colonial Histories
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsIndigenousEmpireSoutheast asiaColonialismDisgustTasteAncient historyCONQUESTHistoryEthnologyPsychologyArchaeologyEcology

Abstract

fetched live from OpenAlex

The durian, for turn of the twentieth century Euro-American travelers in Southeast Asia, seemed a strange fruit – exotic, but unattainable in metropolitan centers. Durian was different than the banana, for example. Colonial companies never grew them in plantations. Even if durians crowded marketplaces from Singapore to Sumatra, the business remained in the hands of native peoples. The fruit matters because the peoples and animals who lived in Malaya and the rest of Southeast Asia loved the durian, enough that tourists – visitors, not colonizers – all noticed it. When Euro-Americans first encountered durians, they were not disgusted; they developed that disgust as the lines of empire hardened. The durian became a spiny site where indigenous tastes and the American and European distaste and suspicion that they brought with them to the colonies clashed. The encounter around durian, the explanation for the fruit’s aromas, the experience of westerners as they forced themselves to overcome its stench, and the way natives openly enjoyed watching the stranger’s first taste of strange fruit all encapsulated in the sensorium the experience of empire: conquest, the articulation of difference, and the careful hidden ways colonized peoples talked back.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.011
Scholarly communication0.0040.003
Open science0.0000.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.252
Teacher spread0.237 · 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 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

Citations14
Published2017
Admission routes1
Has abstractyes

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