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Record W2781015877

Consuming Narratives: Food and Cannibalism in Early Modern British Imperialism

2016· article· en· W2781015877 on OpenAlexaboutno aff
Alisa Marie Wankier

Bibliographic record

VenueeScholarship (California Digital Library) · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCulinary Culture and Tourism
Canadian institutionsnot available
Fundersnot available
KeywordsFoodwaysNarrativeCannibalismHistorySociologyCosmopolitanismAestheticsGender studiesLiteratureAnthropologyArtPoliticsPolitical scienceLawEcology
DOInot available

Abstract

fetched live from OpenAlex

During the early modern period, a time of global exploration, Europeans often included descriptions of foodways in their exploration narratives. Indeed, one of the most striking features of early modern travel narratives is the amount of space devoted to foodstuff and eating patterns, and “Consuming Narratives” argues that Europeans, and specifically the English, focused on food because they understood foreign people and places, themselves, and their world through a discourse of foodways. If an Englishman noted a foreigner eating a specific dish, he might infer the temperament of the foreigner by means of the humoral theory; deduce the wealth or status of the foreigner by the perceived cost of the food; or conclude the civility of the foreigner by his manner of eating or by the way the meal was prepared. Beyond the descriptions of customary foodways, stories of foreign peoples eating human flesh proved to be a recurring theme in which Europeans presented themselves as superior, while recordings of English cannibalism at Jamestown and Newfoundland reflected English anxiety about their position in the global world. Thus, descriptions of foodways reveal more than mere victuals. Food and eating provided a language to express, and simultaneously shape, English assumptions and anxieties about otherness, status, sovereignty, and power.

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 categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.387
Threshold uncertainty score1.000

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 communication0.0010.003
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 head0.013
GPT teacher head0.189
Teacher spread0.176 · 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.

Study designObservational
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

Citations1
Published2016
Admission routes1
Has abstractyes

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