When Differences Unite: Resource Dependence in Heterogeneous Consumption Communities
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".