Urban environmental justice through the camera: understanding the politics of space and the right to the city
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Using the lens of Lefebvre's spatial trialectics, we assess the utility of photo-elicited interviewing for environmental justice, recognising that a view to social spatial analysis is essential to engaging with the historical processes of exclusion and discrimination that are crucial to explaining why unequal distributions of environmental injustice are systemic and not random. Drawing on insights from our own photo-elicited interviewing-based work in the neighbourhood called Parkdale in Toronto, we make two main recommendations for future environmental justice work using photo-elicited interviewing. First, researchers must be open to a broader epistemology, one that draws on a more spatially nuanced and temporally evolving knowledge of the full range of environmental influences on communities. Second, in order to arrive at a more robust critical analysis of social space, researchers should complement photo-elicited interviewing with historical research about the relevant communities and include participants from other comparative communities.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it