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Record W1952937899 · doi:10.1111/1467-954x.12329

Grudge Spending: The Interplay between Markets and Culture in the Purchase of Security

2015· article· en· W1952937899 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Sociological Review · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicTerrorism, Counterterrorism, and Political Violence
Canadian institutionsUniversity of British Columbia
FundersLeverhulme Trust
KeywordsConsumption (sociology)SociologyValue (mathematics)Consumer CultureEconomicsFutures contractParaphernaliaWork (physics)MarketingLaw and economicsBusinessLawSocial scienceAdvertisingPolitical scienceFinancial economics

Abstract

fetched live from OpenAlex

In the paper, we use data from an English study of security consumption, and recent work in the cultural sociology of markets, to illustrate the way in which moral and social commitments shape and often constrain decisions about how, or indeed whether, individuals and organizations enter markets for protection. Three main claims are proffered. We suggest, firstly, that the purchase of security commodities is a mundane, non-conspicuous mode of consumption that typically exists outside of the paraphernalia of consumer culture – a form of grudge spending. Secondly, we demonstrate that security consumption is weighed against other commitments that individuals and organizations have and is often kept in check by these competing considerations. We find, thirdly, that the prospect of consuming security prompts people to consider the relations that obtain between security objects and other things that they morally or aesthetically value, and to reflect on what the buying and selling of security signals about the condition and likely futures of their society. These points are illustrated using the examples of organizational consumption and gated communities. In respect of each case, we tease out the evaluative judgements that condition and constrain the purchase of security among organizations and individuals and argue that they open up some important but neglected questions to do with the moral economy of security.

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.

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)0.007
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.534
Threshold uncertainty score0.533

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.100
GPT teacher head0.432
Teacher spread0.332 · 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