Being from a Bad Neighbourhood: Confronting Bad Decision Discourses in the Impoverished Inner City
Bibliographic record
Abstract
This article confronts mainstream discourses about poverty and inner city poor neighbourhoods. It argues that the ways that poverty and poor inner city neighbourhoods are made publicly known in writing and through visual representations present problems such as overpowering structural causes of health and illness, reifying false dichotomy of us and them, and normalizing people living in poverty or working poor people as de facto vulnerable. This can happen when the social relations that govern poverty and sustain human suffering eschew the social relations that produce these experiences. Taking these relations as the objects of analysis, this article focuses sociologically on the Dundas/Sherbourne neighbourhood in Toronto, Canada, as the terrain of inquiry. The aim here is to contribute to analyses of the political, social, and economic determinants of health as well as to critiques of bad-neighbourhood and bad decision discourses. To do this, it bridges visual practice with critical social analysis: drawing together the authors’ individual practices as visual artists, marshaling their social positions as residents of the adjacent St. James Town neighbourhood, and sharing their experiences of the Dundas/Sherbourne area. They employ insights from sensory ethnography and street photography to offer an alternative source of knowledge about the poor inner city that contrasts and contests mainstream ways of knowing these same 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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.044 | 0.107 |
| Scholarly communication | 0.023 | 0.008 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".