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Record W2284454915 · doi:10.14288/1.0088871

The city as theme park and the theme park as city: amusement space, urban form, and cultural change

2009· article· en· W2284454915 on OpenAlexaff
Stacy Warren

Bibliographic record

VenuecIRcle (University of British Columbia) · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicMedia, Gender, and Advertising
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsTheme parkTheme (computing)AmusementSpace (punctuation)GeographyUrban parkUrban spaceSociologyAestheticsHistoryEnvironmental planningTourismArtArchaeologyPsychologySocial psychologyComputer science

Abstract

fetched live from OpenAlex

Amusement space embodies hegemonic and Utopian dialogue concerning urban conditions. Throughout the twentieth century, two rival urban visions have reigned: the Coney Island model, a chancy, participatory theatre where patrons can confront head-on current conditions; and the Disney model, a carefully planned setting where guests are made to feel comfortable and secure. The current ascendancy of the Disney model, evident in urban and suburban landscapes increasingly shaped in the Disney image, has attracted the attention -- and alarm --of critics who interpret this trend as urban planning with a 'sinister twist.' A case study of Disney's involvement with Seattle Center, originally the site of the 1962 World's Fair and now Seattle's premier urban park, demonstrates, however, that people actively challenge, negotiate, and reform the Disney model to meet their needs by infusing the space with traces of the rival Coney model. The suggestions Disney made for renovation of Seattle Center sparked a city-wide debate that centred on the roles of local participation, cultural sensitivity, and aesthetic design in urban space; Disney was found lacking on all accounts and eventually rejected entirely. Seattle's experience with Disney demonstrates that amusement space offers a rich terrain upon which people can dream about, and implement, urban 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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.867
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.232
Teacher spread0.208 · 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 teacher head, not a consensus.

Study designObservational
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".

Quick stats

Citations2
Published2009
Admission routes1
Has abstractyes

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