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Record W4309522868

Taste, consumption, and markets an interdisciplinary volume

2018· preprint· en· W4309522868 on OpenAlexaff
Zeynep Arsel, Jonathan Bean

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicCulinary Culture and Tourism
Canadian institutionsConcordia University
Fundersnot available
KeywordsConsumption (sociology)TasteVolume (thermodynamics)EconomicsFood scienceChemistryArtAestheticsPhysicsThermodynamics
DOInot available

Abstract

fetched live from OpenAlex

"Taste is a core concept for the social sciences and an orienting notion in everyday practice. It is of equal relevance to academics and laypeople alike. Theorizations of taste are frequently multi- disciplinary, bringing an opportunity to cross-fertilize ideas and concepts. At the same time, a reader, challenged by the diverse body and dispersed nature of theories on taste, needs guidance navigating the literature and framing areas of interest. Until now, those interested in an academic perspective on the concept have had to traverse a wide range of literature. This is the first book that assembles a range of writings on taste from across disciplines to provide the reader with a sense of the emerging and expanding boundaries of this field of study. Taste, Consumption and Markets offers a comprehensive and up-to-date review of taste, with an emphasis on how taste shapes boundaries, subcultures, and global culture, complemented by an introduction that provides a scaffold for the reader and a concluding section that reflects on the past, present, and future of research on taste. It shows the latest state of knowledge on the topic and will be of interest to students at an advanced level, academics, and reflective practitioners. It addresses the topics with regard to the sociology of taste and consumption and will be of interest to researchers, academics, and students in the fields of consumer studies, consumption ethics, sociological perspectives on consumption, and cultural studies"...

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.826
Threshold uncertainty score0.996

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 science0.0000.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.029
GPT teacher head0.277
Teacher spread0.248 · 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

Citations5
Published2018
Admission routes1
Has abstractyes

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