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Record W2058287148 · doi:10.1086/666616

When Differences Unite: Resource Dependence in Heterogeneous Consumption Communities

2012· article· en· W2058287148 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

VenueJournal of Consumer Research · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Marketing and Social Media
Canadian institutionsQueen's University
Fundersnot available
KeywordsMainstreamConsumption (sociology)Resource (disambiguation)Dependency (UML)MarketingFrame (networking)BusinessBridge (graph theory)Public relationsSociologyComputer sciencePolitical scienceSocial science

Abstract

fetched live from OpenAlex

Although heterogeneity in consumption communities is pervasive, there is little understanding of its impact on communities. This study shows how heterogeneous communities operate and interact with the marketplace. Specifically, the authors draw on actor-network theory, conceptualizing community as a network of heterogeneous actors (i.e., individuals, institutions, and resources), and examine the interplay of these actors in a mainstream activity-based consumption community—the distance running community. Findings, derived from a multimethod investigation, show that communities can preserve continuity even when heterogeneity operates as a destabilizing force. Continuity preserves when community members depend on each other for social and economic resources: a dependency that promotes the use of frame alignment practices. These practices enable the community to (re)stabilize, reproduce, and reform over time. The authors also highlight the overlapping roles of consumers and producers and develop a dimensional characterization of communities that helps bridge prior research on brand communities, consumption subcultures, and consumer tribes.

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)0.011
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.642
Threshold uncertainty score0.407

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.002
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 integrity0.0000.001
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 head0.212
GPT teacher head0.427
Teacher spread0.215 · 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