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Record W2738299647 · doi:10.1177/1469540517717779

Reading a water menu: Bottled water and the cultivation of taste

2017· article· en· W2738299647 on OpenAlexafffund
Andrew Biro

Bibliographic record

VenueJournal of Consumer Culture · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsAcadia University
FundersAcadia University
KeywordsBottled waterTasteCapitalismCommodificationConsumption (sociology)CommoditySociologyEconomicsEconomyPolitical scienceSocial scienceLawMarket economyFood scienceEnvironmental scienceChemistry

Abstract

fetched live from OpenAlex

The market for bottled water is growing and increasingly segmented. How do we explain not just the willingness to pay for a substance (water) that is almost free but also the increasing discernment in a drink generally considered tasteless? We argue that bottled water market segmentation is a leading edge of processes of water commodification, associated with the crisis of Fordism and rise of consumerist capitalism, where the assertion of status through commodity consumption is increasingly necessary. The extensive Ray’s & Stark water menu is analyzed to show how the taste for bottled waters is cultivated. In the menu, references to gustatory sensation are limited. Instead, the tastefulness of water inheres in the distance from anthropogenic influence, made visible through scientific (geological) discourses. The tension between the desire to consume unmediated nature and the scientific abstraction necessary to recognize it reveals the social character of the taste for bottled waters. The highly refined sense of taste that the water menu’s readers are presumed to have is a reflection of consumerist capitalism’s distinctive ways of reproducing socio-economic inequality and metabolizing non-human nature.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.817
Threshold uncertainty score0.774

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
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 head0.021
GPT teacher head0.322
Teacher spread0.301 · 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
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

Citations17
Published2017
Admission routes2
Has abstractyes

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