Bibliographic record
Abstract
Reviewed by: SubUrbanisms: Casino Urbanization, Chinatowns, and the Contested American Landscape by Stephen Fan, and: Chinese Style: Rediscovering the Architecture of Poy Gum Lee, 1923–1968 by Kerri Culhane June Williamson (bio) Stephen Fan, curator SubUrbanisms: Casino Urbanization, Chinatowns, and the Contested American Landscape Kerri Culhane, curator Chinese Style: Rediscovering the Architecture of Poy Gum Lee, 1923–1968 Exhibitions at the Museum of Chinese in America, New York, September 24, 2015 through March 27, 2016. With SubUrbanisms and Chinese Style, the Museum of Chinese in America has staked a broadened claim for the role of Asian immigrants—Chinese Americans in particular—in the production of metropolitan cultural landscapes beyond traditional Chinatowns. The museum, founded in 1980 and housed in lower Manhattan since 2009 in a greatly expanded space designed by Maya Lin, is an ideal location to examine the material in these concurrent exhibitions. The larger of the two shows, SubUrbanisms, invites the visitor to travel from crowded Mott and Canal Streets in Manhattan to the west bank of the Thames River in eastern Connecticut, where sits the Mohegan Sun, a megacasino resort. Dozens of buses travel to the casino from Chinatown—and from every other Chinatown in the Northeast—daily; many cater to gamblers from Asian communities, as evidenced by radiating red threads in one of a series of provocative diagrams and mappings that provide spatial and temporal context in the exhibition, curated by architect Stephen Fan with graphic design by Shane Keaney. Originating in 2014 as SubUrbanisms: Casino Company Town/China Town at the Lyman Allyn Art Museum in New London, Connecticut (winner of VAF’s 2015 Buchanan Award), the show is accompanied by a graphics-packed catalog with contributions from Abigail Van Slyck, Chloe Taft, Ellen Pader, and Aron Chang.1 The exhibition (like the book) is organized around four primary themes: “ Casino Sub-urbanization,” “Learning from Los Chinos,” “Single/Multi Family Hybrid Housing,” and “Sub-Urban Urbanism.” It features large photographs of places and people, infographic charts and maps, full-scale mockups of domestic spaces, documentation of houses in plan and model, and hypothetical architecture and urban design proposals by Fan. Robert Venturi, Denise Scott Brown, and Steven Izenour’s symbolic readings of Las Vegas and Levittown are obvious touch-stones to the design research into casino landscapes and postwar ranch houses.2 The “kind of looking” provided here, however, is markedly different, as Van Slyck notes in the catalog.3 As the subtitle of Learning from Las Vegas reminds us, Venturi and Scott Brown sought to highlight and mine the “forgotten symbolism of architectural form” in ordinary commercial and domestic buildings. Fan, by contrast, redirects our gaze to subtle signs of hidden daily practices and cultural preferences that reframe, trouble, and subvert the symbols on the surface, likening the built environment to a “prism” through which to understand larger dynamic forces.4 The Mohegan Sun casino, complete with a thirty-four-story hotel tower, was built on a Super fund site formerly used by a manufacturer of casings for submarine nuclear reactors. The exhibition (like the catalog) situates this suburban mega casino in various narratives. One is the history of company towns, including nearby Norwich, once a center of textile production by Irish and French Canadian immigrant workers. Another is the [End Page 138] growth and dispersion of Asian immigrants in the United States, who comprise a hefty proportion of both casino patrons and workers (Fan estimates 20 to 30 percent and 10 to 25 percent, respectively, at Mohegan Sun). A third is the recent explosive growth of casino development—on Native American reservations, on postindustrial sites, and along state and national borders. A Native American– owned redevelopment, the Mohegan Sun is not subject to local permitting and public approvals processes. Large color photographic prints, one reproduced on the catalog cover, highlight how the shimmering glass tower seems to rise out of the tree canopy, like a singularity. Obscured in the glossy images are the local, regional, and indeed global footprints of the thousands of workers necessary to sustain the mirage. “Learning from Los Chinos” picks up their story. Fan maps the constellation of businesses that are Asian owned and/or cater to Asian customers...
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.710 | 0.644 |
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".