Bibliographic record
Abstract
Abstract Austerity has become a key consideration for studying ongoing state restructuring of the urban since the economic crisis of 2008. However, academic debates have yet to fully interrogate the role of race in this process. This article reviews geographic literature on race and austerity. It outlines the emergence of austerity urbanism, and the geographic, sociological, and political science literatures from which it draws its origins from. Focusing on the interplay between race/racialization and austerity, this article engages with critical theories of race to better understand the “raced” nature of austerity, and how these processes shape cities. Critical theories of race have been influential in linking race to forms of state power and governance in geography, while also exploring racism as a material and discursive formation that is connected across space and time by capital. Austerity urbanism literature has yet to develop a sophisticated analysis of the racialized dimensions of austerity in the U.S. context. Rather, scholarship up to date theorizes race through fixed categories, where racialized groups are seen and mapped onto austerity policy outcomes. In this paper, I propose that critical theories of race can provide an analytical framework for geographers to better understand the relations between race and austerity through the lens of racial capitalism by revealing how periods of neoliberalization are organized along racial lines and operate through and upon terrains of racial domination and empire. This means framing race and racism as a process (i.e., racialization) that is inextricably embedded in the logic of the neoliberal project. This paper concludes with commentary on possible future directions, both empirical and conceptual, that engagements with racial capitalism can offer to the literature on austerity urbanism to interrogate race, power, and justice across the Global North and South.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".