How Do Physical Disability Cues Influence Assumptions about Consumer Tastes? Unpacking the Disability Preference Stereotype
Bibliographic record
Abstract
Abstract Across 10 experimental studies, this research identifies and provides evidence of a disability preference stereotype whereby observers infer that disabled consumers prefer utilitarian products more than nondisabled consumers and prefer hedonic products less than nondisabled consumers. We show that this stereotype occurs because of societal associations between physical disability and pity. Pity elicits a multidimensional response such that considering the interests of a disabled person increases feelings of personal discomfort, driving both an inclination to help (help-giving orientation) and a tendency to assess the perceived misfortune (misfortune appraisal) in parallel. Thus, when considering the preferences of disabled individuals, the help-giving orientation increases focus on functional (utilitarian) goods, while the misfortune appraisal decreases focus on pleasurable (hedonic) goods. Importantly, this stereotype can be mitigated through increased disability representation. Representation of empowered disabled individuals in media can dampen the help-giving orientation, reducing inferred utilitarian preferences, while representation of disabled people partaking in daily pleasures through increased accessibility can reduce misfortune perceptions, increasing inferred hedonic preferences. This work addresses the paucity of disability-related consumer research, identifies how aspects unique to consumption can limit consumers with disabilities, and highlights opportunities to minimize ableist stereotypes by expanding representation and increasing marketplace inclusion.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".