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Record W2790697304 · doi:10.1111/ijcs.12428

Consuming counterfeit: A study of consumer moralism in China

2018· article· en· W2790697304 on OpenAlexaff
Eric Ping Hung Li, Magnum Lam, Wing‐sun Liu

Bibliographic record

VenueInternational Journal of Consumer Studies · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Behavior in Brand Consumption and Identification
Canadian institutionsUniversity of British Columbia, Okanagan CampusKelowna General HospitalUniversity of British Columbia
FundersHong Kong Polytechnic University
KeywordsCounterfeitConsumption (sociology)MoralitySociologyConsumer behaviourConstruct (python library)NegotiationMarketingValue (mathematics)ChinaSocial psychologyAdvertisingBusinessPsychologyPolitical scienceSocial scienceLaw

Abstract

fetched live from OpenAlex

Abstract The consumption of counterfeits is a central theme in understanding consumer moralism. While some studies on marketing have highlighted the consumption motives and socio‐economic factors behind this seemingly unethical phenomenon, research on the subjective experiences of consumers and the cultural concerns about the consumption of counterfeits is lacking. The aim of this article is to gain a better understanding of how consumers construct and negotiate their moralistic identities through engaging in counterfeit consumption. We also examine how consumers utilize counterfeit goods as symbolic resources to echo, or even reproduce, the entrenched Chinese social relationships and marketplace ideological conditions. Our findings suggested that the research participants attempted to make sense of their counterfeit consumption behaviour by infusing the moralistic meanings drawn from the Chinese socio‐cultural value orientation. The study concludes that the moral identity work and counterfeit consumption practices are interwoven in a web of multiple discourses and resources available in the contemporary marketplace under the overarching consumer moralism framework.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score0.731

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.059
GPT teacher head0.349
Teacher spread0.290 · 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 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

Citations23
Published2018
Admission routes1
Has abstractyes

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