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Record W4390942978 · doi:10.35483/acsa.am.110.86

Mixing Metabolisms: New People in Aging Sprawl

2022· article· en· W4390942978 on OpenAlexaboutno aff
Lawrence B. Davis

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsnot available
Fundersnot available
KeywordsUrban sprawlEthnic groupImmigrationHuman settlementEconomic geographySettlement (finance)Built environmentPolitical scienceGeographyUrban planningEconomic growthSociologyBusinessEngineeringCivil engineeringEconomics

Abstract

fetched live from OpenAlex

The historically white postwar suburbs of the United States and Canada are now the first destination for new residents from abroad. Because they are relatively safe, affordable, and, as they became ethnic enclaves, culturally familiar, these peripheral communities, have gradually replaced the center city as the desired landing place for new arrivals. Using scholarly and popular literature with empirical field observation, this paper examines a set of aging postwar suburbs in California and one in Arizona to illustrate the largely positive effects of such immigration. In addition to an injection of diverse and energized cultures, the changes can point towards a set of materially strategic options for renewing all aging low density-built environments. Such cultural shifts are an opportunity to imagine a socially healthy, though repurposed, and spatially altered, future for all aging urban peripheries. Finally, regarding the development of a productive architectural and urban discourse on the subject, it is vital to note that the current nascent transformation in aging postwar environments is common to that of many cities and settlements throughout history. More specific to the case of renewal of postwar sprawl with its’ increase in the density of social interaction, the current ethnic changes to the settlement pattern often make the largely private built environments of Anglo-American suburbs more “urban” and in the process reveal a more nuanced definition of this term.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.008
Scholarly communication0.0060.006
Open science0.0010.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.026
GPT teacher head0.293
Teacher spread0.267 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2022
Admission routes1
Has abstractyes

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