Navigating the Central Tensions in Research on At-Risk Consumers: Challenges and Opportunities
Bibliographic record
Abstract
A perennial problem in social marketing and public policy is the plight of at-risk consumers. The authors define at-risk consumers as marketplace participants who, because of historical or personal circumstances or disabilities, may be harmed by marketers’ practices or may be unable or unwilling to take full advantage of marketplace opportunities. This definition refers to either objective reality or perceptions. Early research focused on consumers who were at risk because they were poor, ethnic or racial minorities, immigrants, women, or elderly. Today's researchers also study consumers who are at risk because they are from religious minorities, disabled, illiterate, homeless, indigent, lesbian, gay, bisexual, or transgender. The authors identify four tensions affecting research on and policy and marketing applications for at-risk populations: the value of focusing on (1) vulnerabilities versus strengths, (2) radical versus marginal change, (3) targeting versus nontargeting, and (4) encouraging knowledgeable versus naive consumers. They conclude with a discussion of the significance of including at-risk consumers as full marketplace participants and identify future research directions.
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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 | 0.492 | 0.300 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.021 | 0.102 |
| Scholarly communication | 0.043 | 0.078 |
| Open science | 0.009 | 0.029 |
| Research integrity | 0.028 | 0.032 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".