Bibliographic record
Abstract
Abstract Neoliberalization processes have been reshaping the landscapes of urban development for more than three decades, but their forms and consequences continue to evolve through an eclectic blend of failure and crisis, regulatory experimentation, and policy transfer across places, territories and scales. The proliferation of familiar neoliberal discourses and policy formulations in the aftermath of the 2007‐09 world financial crisis masks evidence of more deeply rooted transformations of policies, institutions and spaces that continue to combatively remake terrains of urban development. Accordingly, the critical intellectual project of deciphering the problematic of neoliberal urbanism must continue to evolve. This essay outlines some of the methodological and political challenges associated with (re)constructing a ′moving map′ of post‐crisis neoliberalization processes. We affirm a form of critical urban theory that adopts a restlessly antagonistic stance towards orthodox urban formations and their dominant ideologies, institutional arrangements and societal effects, tracking their endemic policy failures and crisis tendencies while at the same time demarcating potential terrains for heterodox, radical and/or insurgent theories and practices of emancipatory social change.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.049 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".