Model city failure: New Haven and limits to the entrepreneurial state
Bibliographic record
Abstract
This paper critically examines the concept of the entrepreneurial state by focusing on the case study of New Haven, Connecticut, during the mid-20th century. We delve into the ambitious urban development efforts spearheaded by Mayor Richard Lee and his administration. In particular, we examine the construction of the Chapel Square Mall as a response to urban decline with the hope it would stimulate economic growth. Through a comprehensive analysis of historical records, political dynamics, and the socioeconomic context of New Haven, we highlight the inherent challenges and limitations faced by local governments in acting as entrepreneurs. Our findings reveal that despite the strategic intentions to revitalize the city and foster an entrepreneurial ecosystem, the project ultimately resulted in substantial financial losses, displacement of communities, and failed urban planning ambitions. This case study underscores the complexities of state entrepreneurship at the local level, where political motives, limited information, and the pursuit of voter support can diverge significantly from entrepreneurial objectives, leading to outcomes that are detrimental to both the city’s economic health and its residents. By comparing New Haven’s experience with broader critiques of the entrepreneurial state, this paper contributes to a nuanced understanding of the potential and pitfalls of government-led economic development initiatives.
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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 | 0.003 | 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 | 0.016 | 0.033 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| 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".