Imagining the urban other: Place, abjection, and public views of risk
Bibliographic record
Abstract
This paper examines the relationship between individual feelings of aversion, fear, and disgust of city spaces and broader systems of cognitive urban zoning. We analyze interviews conducted in four distinct urban areas of Ottawa, Canada, working with an open-ended method to learn about how urban individuals understand the concept of “risk.” We identify fear of crime as a central risk perceived by the respondents and observe how they construct boundaries between themselves and perceived “risky” zones, occurrences, and bodies. Drawing from Kristeva’s theory of abjection, we trace a semiotic system of Othering in the respondents’ narratives, examining the symbolic cleansing that occurs when respondents attempt to differentiate themselves from what they perceive as encroaching Otherness. With focus on claims about four distinct neighbourhoods, we argue that risk in the city is configured through physical and imaginative mobilities, through which inhabitants construct boundaries and attempts to cleanse or purify “risky” spaces. We conclude that the sense of abjection and/or the experience of aversion is a way that fear is mapped onto cities. This research shows how city spaces are zoned through fear-based semiotic systems. We also raise questions about the relationship between these semiotic systems and actual tangible threats in these spaces.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.042 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".