Racial capitalism in urban studies: From spaces of victimisation to spaces of benefit
Bibliographic record
Abstract
The burgeoning growth of racial capitalism work within urban studies (RCUS) has garnered considerable attention. In this critical commentary, we embark on an examination of existing scholarship to ascertain its theoretical relevance within this domain. Our inquiry reveals a predominant focus on the plight of individuals ensnared in the web of everyday racial capitalism. The existing body of work predominantly directs its gaze towards what we term ‘spaces of victimisation’, while largely neglecting those who derive advantages from this system. Transcending from the study of victimisation to the exploration of spaces characterised by benefit presents formidable challenges. We consider some of the challenges to making the leap from spaces of victimisation to spaces of benefit: the routineness of benefit, the scale(s) of benefit, and the remoteness of benefit. In sum, we suggest how the application of RCUS might confront these multifaceted challenges, offering a unique vantage point for critical analysis.
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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 | 0.021 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.018 | 0.070 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.005 | 0.009 |
| 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".