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Record W1987855153 · doi:10.2747/0272-3638.24.6.479

The 90S Show: Culture Leaves the Farm and Hits the Streets<sup>1</sup>

2003· article· en· W1987855153 on OpenAlexaff
Trevor J. Barnes

Bibliographic record

VenueUrban Geography · 2003
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Industries and Urban Development
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCultural geographyUrban theoryMarxist philosophySociologyUrban geographyHuman geographyPostmodernismUrban planningEconomic geographySocial scienceGeographyPolitical scienceLawEpistemologyPhilosophyCivil engineering

Abstract

fetched live from OpenAlex

The tradition of American cultural geography was defined by studies of the rural, and in its more prosaic form focused on cataloging and mapping artifacts such as fence posts, barn types, and gravestones in order to delimit culture areas. In contrast, the city was all but ignored, treated as a cultural vacuum, and conceived only as a site of work, production, and economic relations. Hence, the importance of urban spatial science that from the late 1950s formalized that economism as central place theory, or Alonso's map of bid-rent curves, or models of retail location. Even when urban geography began eschewing formal models and theory, turning toward some kind of Marxist approach during the 1970s, the focus on things economic remained, but couched now in a different vocabulary such as rent gap, urban gatekeepers, and uneven development. The economism of spatial science and Marxism could not be sustained, however. Culture had to be let in. From 1990 pressured by outside theoretical developments such as cultural studies, and postmodernism, and changes from inside the discipline such as the rise of the new cultural geography, urban geography finally cracked, explicitly allowing culture in first as a trickle, but by the end of that decade as a flood. Culture had finally left the farm and hit the streets.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.388
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0040.001
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.017
GPT teacher head0.239
Teacher spread0.223 · 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 designNot applicable
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

Citations13
Published2003
Admission routes1
Has abstractyes

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