Managing territorial stigmatization from the ‘middle’: The revitalization of a post-industrial Business Improvement Area
Bibliographic record
Abstract
Wacquant’s territorial stigmatization concept asserts that state/private actors mobilize discourses of stigmatization about specific areas in a city in order to legitimize spatial solutions in an attempt to solve complex political-economic problems. Unlike conventional studies of territorial stigmatization which delineate the concept between the production of stigma from ‘above’ and the resistance of stigma from ‘below,’ this paper contributes the concept of territorial stigmatization from the ‘middle’. Given their conceptualization as key players in the urban assemblage, we specifically examine how Business Improvement Areas (also known as Business Improvement Districts in the U.S) negotiate territorial stigmatization throughout the neighbourhood revitalization process. We highlight Business Improvement Areas’ unique middle position by drawing on data collected from interviews, media articles, and urban planning reports in London Ontario’s Old East Village over a fifteen-year period. In short, we find the use of territorial stigmatization by Business Improvement Areas is contingent upon their relationship within the urban assemblage (both actors from above and below).
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.
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.021 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".