Cycles of inequality in the marketplace: Insights from macro, marketer, and consumer perspectives
Bibliographic record
Abstract
Seeking inequality via differentiation is a fundamental theme in the marketing literature: consumers derive utility from products that convey socially valued attributes, and marketers target consumers by giving them opportunities to differentiate on socially valued attributes. However, as a large body of evidence shows, inequality can reduce consumer well-being and limit economic growth. In this paper, we take a systemic view of marketplace inequality, examining the interdependence among consumers, marketers, and macro forces in shaping inequality in markets for goods and services. Our broad review of the marketing literature across ten marketing journals and a variety of subdomains within the field (e.g., macromarketing, consumer behavior, marketing strategy, quantitative marketing) suggests that macro forces, marketers, and consumers are all part of a dynamic system in which each contributes to creating, perpetuating, and disrupting cycles of marketplace inequality. By highlighting the process by which inequality can be created, perpetuated, and reduced, we hope to give marketing researchers and practitioners insight into interventions that have the potential to increase consumer well-being and marketer profitability.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".