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Record W7168836

Section 2: Landscapes of Capital - On Big Beaver Road: Detroit and the Diversity of American Metropolitan Landscapes

2007· article· en· W7168836 on OpenAlexaboutno aff
Robert Fishman

Bibliographic record

VenueeScholarship (California Digital Library) · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicUrbanization and City Planning
Canadian institutionsnot available
Fundersnot available
KeywordsMetropolitan areaGeographyUrban sprawlEconomic geographyBeaverCraftDeindustrializationReal estateDiversity (politics)Urban planningArchaeologyCivil engineeringPolitical scienceEngineeringEcology
DOInot available

Abstract

fetched live from OpenAlex

On Big Beaver Road: Detroit and the Diversity of American Metropolitan Landscapes Robert Fishman A century ago each major metropolitan region in the United States had its own distinctive landscape—its unique synthesis of geography and regional building types. From the tenements and brownstones of New York, to the four-flats and bungalows of Chicago, to the wood-framed “painted ladies” of San Francisco, American cities gloried in the small-scale, craft-oriented building industries and localized systems of finance that built their unique identi- ties. At the same time, however, overall regional form was, within the limits of differing geographies, surprisingly uniform, as American metropolitan regions converged around the model of the “centralized industrial metropo- lis” most clearly expressed in Chicago. Every major met- ropolitan region possessed (or aspired to possess) a version of Chicago’s Loop or central business district, surrounded by a “factory zone” with worker’s housing, and finally a small but prosperous “suburban ring.” Today, we are in the opposite position: building types are stultifyingly similar nationwide, yet the overall forms of metropolitan regions have become surprisingly diverse. This new combination of similarity and difference makes it difficult to “read” American metropolitan land- scapes today. It is all-too-easy to perceive nothing but undifferentiated sprawl—the coast-to-coast recurrence of Christopher Leinberger’s “nineteen standard real estate product types,” spread over an equally-standardized low- density, fragmented, automobile-dependent landscape. 1 But the very pervasiveness of this sprawl tends to hide vastly differing landscapes at the metropolitan scale. For example, a recent study of average regional densities ranked New York and Los Angeles as our two densest regions—but, of course, for opposite reasons. New York still possesses the older pattern of a sharply falling “density gradient,” whereas Los Angeles has only recently filled in to a relatively constant density over a vast urbanized area. 2 Similarly, a recent study of “job sprawl” paired Detroit with Tampa-St. Petersburg-Clearwater as two notable examples of “extremely decentralized employment metros.” But, again, the causes are starkly different. Both earn their designation as “extremely decentralized” by having only 5 percent of their jobs within three miles of the regional core, and more than 75 percent of the jobs ten miles or more from the core. But where Tampa-St. Peters- burg-Clearwater was “built decentralized,” with recent explosive job growth spread out through an extended region, Detroit as late as 1950 expressed even more strik- ingly than Chicago the basic form of the centralized industrial metropolis, with more than three-quarters of the region’s jobs and population tightly concentrated in the central city. Half a century of “urban crisis,” however, has devastated Detroit’s downtown and depopulated and dein- dustrialized its vast, once-thriving “factory zone,” so that more than three-quarters of the remaining jobs are now located outside the central city. 3 The Divided Metropolis In this kaleidoscope of metropolitan landscapes, Detroit has the melancholy distinction of faithfully following into the twenty-first century the “urban crisis” trajectory that seemed the fate of all older American cities in the 1960s and 1970s. In spite of continuous large-scale private/public investment downtown—ranging from a John Portman- designed massive hotel/office complex called the Renais- sance Center (1978), to a constantly expanding convention center, to new baseball and football stadiums, to four new gambling casinos—the Detroit downtown remains largely derelict, a landscape of abandonment that led photog- rapher Camilo Vergara to call for preserving it intact as a “national ruins park.” 4 Even worse is the surrounding factory zone, where the brownfield landscape is unparal- leled nationwide in its size and devastation. These zones of abandonment stretch almost uninter- ruptedly from downtown to the city line, making the famous “8 Mile Road” a more salient border for the region than the nearby international boundary between the United States and Canada. What this border expresses most clearly is that Detroit is the most segregated of American metro- politan regions: crossing 8 Mile means going from a central city that is 87 percent black to neighboring white working- class suburbs that are less than 2 percent minority. 5 Pros- perity also suddenly appears—both the well-maintained tract houses of highly paid automobile workers (the “blue- collar aristocracy”) in Macomb County, and the more sub- stantial affluence of neighboring Oakland County, where such suburbs as Birmingham, Troy, and Bloomfield Hills are among the wealthiest in the nation. Although the city of Detroit has declined from a peak population of nearly 2 million in 1950 to 850,000 today, the region as a whole has never shrunk, and is now home to more than 4 million people. Yet no other metropolitan region in the developed world perhaps shows so stark a pattern of concentrated poverty and concentrated affluence. 6 Although there is no single explanation for the hyper- intensity of Detroit’s divided metropolis, I would identify at least two elements that distinguish it even from other Rustbelt regions. Detroit’s sudden rise from a second- ary city in the late nineteenth century to world leadership only thirty years later as “the Motor City” meant that its Fishman / On Big Beaver Road

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.115
Threshold uncertainty score0.228

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0060.003
Scholarly communication0.0060.003
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0270.002

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.

Opus teacher head0.012
GPT teacher head0.223
Teacher spread0.211 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

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Citations0
Published2007
Admission routes1
Has abstractyes

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