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Record W2794408642 · doi:10.1080/04353684.2018.1428495

Suburban policy mobilities: examining North American post-war engagements with Vällingby, Stockholm

2018· article· en· W2794408642 on OpenAlexaboutno aff
Ian R. Cook

Bibliographic record

VenueGeografiska Annaler Series B Human Geography · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical and Economic history of UK and US
Canadian institutionsnot available
Fundersnot available
KeywordsMobilitiesSuburbanizationMetropolitan areaSociologyUrban policyPolitical scienceEconomic geographyUrban planningPublic administrationGeographySocial scienceEngineeringCivil engineeringArchaeology

Abstract

fetched live from OpenAlex

This article calls for a detailed examination of the links between suburbia and the mobilization of policy knowledge. With suburbanization taking place across the world and the expanding literature on policy mobilities having little to say about the suburbs, this article begins to address this important gap in our collective understanding. It does this through a case study of Vällingby, a Stockholm suburb that captured the imagination of many planners and architects outside of Sweden during the 1950s and 1960s. Here the article considers the variegated ways in which planners and architects in North America engaged with Vällingby and used lessons learnt from Vällingby in their working practices. It focuses on the encounters with Vällingby by the New York-based architect Clarence Stein as well as those involved in the planning of the metropolitan Toronto suburb of Flemingdon Park. In so doing, the article demonstrates that suburbs are important sites within the circulation of policy knowledge and that audiences elsewhere engage with such sites in a multiplicity of ways. It also challenges a perception of the USA as an exporter and not an importer of suburban ideas and models.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.457
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.006
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.023
GPT teacher head0.272
Teacher spread0.249 · 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; both teacher heads agree on what is shown here.

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

Citations10
Published2018
Admission routes1
Has abstractyes

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