Bibliographic record
Abstract
As the first installment of a two-part article exploring contemporary transformations in metropolitan governance in the wake of the entrepreneurial turns of the 1980s and subsequent waves of neoliberalisation and financialisation, a case is outlined here for a ‘conjunctural’ approach to urban analysis. This can be considered to be complementary to, but at the same time distinct from, some of the concurrent approaches to comparative urbanism, in that it explicitly problematises the relative positioning of cities in the context of uneven development and multiscalar relations, as well as the dialogic connections between case studies, midlevel concepts and revisable theory claims. Taking as its point of departure the current financial and political crisis in Atlantic City, the New Jersey casino capital, the article historicises the concept of the entrepreneurial city, placing this in the context, successively, of the evolving ‘commonsense’ of neoliberal governance, the emergence of austerity urbanism and the intensification of financialised restructuring. In tracing an arc from more abstract theory claims through to the specific circumstances of contemporary urban restructuring in the United States, the article sets the stage for the more granular and concrete analysis of ‘late-entrepreneurial’ Atlantic City to follow in Part 2. To the extent that it is necessary to construct some of this staging, this first part of the article reflects on some of the methodological implications of a conjunctural approach to urban studies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.006 |
| 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".