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Record W1487489209 · doi:10.26503/todigra.v1i3.31

SimCity and the Creative Class: Place, Urban Planning and the Pursuit of Happiness

2014· article· en· W1487489209 on OpenAlexaff
Frederika A Eilers

Bibliographic record

VenueTransactions of the Digital Games Research Association · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRural development and sustainability
Canadian institutionsMcGill University
Fundersnot available
KeywordsWrightSociologyHappinessCriticismValue (mathematics)Class (philosophy)AestheticsEpistemologyPsychologySocial psychologyComputer sciencePolitical scienceArtLaw

Abstract

fetched live from OpenAlex

Used habitually in educational settings, SimCity has been drawing many young people to design by highlighting popular aspects of urban planning. The 2007 version of the game mimics popular planning theories that resemble the controversial work of Richard Florida and his use of the creative class. Florida's writings are in this article interlinked with texts produced by Will Wright, creator of SimCity, as well as the game`s websites, manuals, in order to track these similarities. It is my understanding that both Florida's and Wright's work share and emphasize certain cultural values, including cities' personalities. The analysis reveals how significantly the existence of happiness is linked to place in contemporary cultural setting, although Florida and Wright seem to disagree on how exactly they may relate. Furthermore, critiques of Florida also evoke criticism of the game’s suppositions. Through interpreting SimCity’s application in pedagogy, its educational value is tied to discussions of in-game assumptions which promote academic critical inquiry. The conclusion frames the game as a simulation or model in game and play theory and problematizes Wright's intention to build elaborate models based on assumptions, which players as potential urban planners absorb and emulate.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.449
Threshold uncertainty score0.273

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.018
GPT teacher head0.259
Teacher spread0.241 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations5
Published2014
Admission routes1
Has abstractyes

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