The Dilemmas of Spatializing Social Issues
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
Cities have established official neighborhood boundaries for targeted social policy in recent decades. The authors propose that a sociological conception of neighborhoods sensitizes us to the potential consequences of imposing categorical divisions onto a largely continuous urban space. The authors specify this idea in three steps. First, they argue that designations affect people’s behavior toward target neighborhoods. Second, the heterogeneity within official boundaries may lead to informational distortion; disadvantaged areas are denied benefits solely because of location. Third, designations may generate negative reputations for targeted areas or extend existing stigma to new areas. To examine these processes, the authors study Toronto’s Priority Area Program (2006–2013). Difference-in-difference models show significant negative effects of the designation on rent, home value, and building permits. The authors provide evidence of informational distortion through income distribution analysis. An analysis of policy documents, newspaper reports, and secondary literature illustrates the stigmatizing aspects that local community members and observers interpreted about the designation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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