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Record W2765291861 · doi:10.1177/0038038517726646

Use Value as a Cultural Strategy against Over-Commodification: A Durkheimian Analysis of Craft Consumption within Virtual Communities

2017· article· en· W2765291861 on OpenAlexaff
Steven James Cole

Bibliographic record

VenueSociology · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Industries and Urban Development
Canadian institutionsBishop's University
Fundersnot available
KeywordsCraftCommodificationSociologyAnomieConsumption (sociology)IndividualismCommodityValue (mathematics)MetaphorAestheticsLawEconomicsSocial sciencePolitical scienceAnthropologyEconomyVisual arts

Abstract

fetched live from OpenAlex

Craft consumption is a precarious practice since consuming mass commodities can inadvertently reinforce the overly commodified world craft consumers are seeking to balance and address. Conceptualizing guitar players as craft consumers, this article illustrates the struggle for balance within musical virtual communities. Virtual communities’ commodity discussions can fuel commodity desire and foster a type of consumptive anomie that members call ‘GAS’ (Gear Acquisition Syndrome). This anomie is countered by images, norms, and evaluative standards that critically invoke ‘use value’ to regulate member’s consumption. Using Durkheim’s concepts of ‘social facts’ and ‘discipline’, the article examines how online actors ‘use use value’ to curb their anomic consumption, differentiate craft from mass consumption, and critique problematic consumptive acts. Durkheim’s work, however, illuminates how Campbell’s individualistic and psychological account of consumptive desire limits his own analysis of craft consumption, for such an individualistic understanding cannot account for the collective phenomenon witnessed throughout this article.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0060.030
Scholarly communication0.0060.006
Open science0.0010.004
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 head0.195
GPT teacher head0.393
Teacher spread0.198 · 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 designQualitative
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

Citations9
Published2017
Admission routes1
Has abstractyes

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