Disgust sensitivity and perceptions of urban density and densification
Bibliographic record
Abstract
Purpose This paper examines whether urban density perceptions are associated with disgust. Understanding variation in density perceptions is important as densification is increasingly proposed as an urban intervention due to growing awareness of the impacts of climate change and sprawl. Design/methodology/approach The study was conducted in a decision science laboratory, with participants responding to two visual preference surveys and two narrative scenarios. Participants’ disgust sensitivity was empirically assessed using the revised disgust scale (DS-R), a questionnaire widely used to measure disgust. The research question is whether there is an association between disgust and density perceptions. The paper draws on historical examples to argue that disgust is conceptually relevant to attitudes towards density. Findings The results show statistically significant associations between disgust and some density measures. Participants with higher disgust sensitivity found the highest density images shown significantly less appealing and found less crowded outdoor settings more appealing. This suggests that settings involving high densities or a feeling of crowding may elicit more negative responses from those with higher disgust sensitivity. The paper concludes that disgust may be an overlooked consideration for urban planners, designers and policymakers. Originality/value There has been little study of the relationship between disgust and density perceptions. The findings serve as a call for further research on how emotions, including disgust, affect responses to density and other built environment features. Too little attention has been given to emotions’ role in urban planning and design and these fields can benefit from greater dialogue with insights from psychology and the behavioural sciences.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".