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Comfort Food

2017· book· en· W4246540504 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity Press of Mississippi eBooks · 2017
Typebook
Languageen
FieldAgricultural and Biological Sciences
TopicCulinary Culture and Tourism
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)Ethnic groupTourismSociologyAdvertisingPsychologyGeographyBusinessAnthropology

Abstract

fetched live from OpenAlex

As a subject of study, “comfort food” is relevant to a number of scholarly disciplines, most obviously food studies, folkloristics, and anthropology, but also American culture studies, cultural studies, global and international studies, tourism, marketing, and public health. This volume explores the concept of “comfort food” primarily within a western context with examples from Atlantic Canada, Indonesia, England, and various ethnic, regional, and religious populations as well as rural and urban residents in the U.S. It includes studies of a wide range of dishes—bologna to chocolate, sweet and savory puddings, fried bread with an egg in the center, dairy products, fried rice, cafeteria fare, sugary fried dough, soul food, and others—exploring ways in which they comfort or in some instances cause discomfort and how they are connected to a sense of emotional well-being. Some essays analyze the phenomenon in daily life; others consider comfort food in the context of cookbooks, films, Internet blogs, literature, marketing, and tourism. Recognizing that what heartens one person might discomfort another, the collection is organized accordingly, from pleasant and comforting to unpleasant or discomforting food experiences. Those foods and food experiences are then related to concepts and issues such as identity, family, community, nationality, ethnicity, class, sense of place, tradition, stress, health, discomfort, guilt, betrayal, and loss, contributing to a deeper understanding of comfort food as a significant social category of human behavior.

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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.208
Threshold uncertainty score0.482

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 science0.0010.000
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 head0.031
GPT teacher head0.189
Teacher spread0.158 · 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
GenreOther

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

Citations10
Published2017
Admission routes1
Has abstractyes

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