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Vertical suburbs

2023· book-chapter· en· W4321479768 on OpenAlexaboutno aff
Carl Abbott

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicUrbanization and City Planning
Canadian institutionsnot available
Fundersnot available
KeywordsCityscapeBeijingModernization theoryConurbationGeographyEconomyImmigrationWorld War IIPolitical scienceEconomic historyChinaHistoryArchaeologyVisual arts

Abstract

fetched live from OpenAlex

Abstract Many countries have chosen to build their suburbs upward, with high-rise apartments, rather than outward, with low-rise terrace housing and single-family houses. Since World War II, this has been a common choice when national governments have taken the lead in providing for exploding urban populations, and this chapter focuses on three areas in which this approach was dominant. The Soviet Union and its Warsaw Pact allies shared a common style, using precast concrete slabs to build clusters of eight- or ten-story towers with small, standard apartments for immigrants from the countryside. Western European nations such as Sweden and especially France did much the same, creating the banlieue cityscape of suburban Paris. Singapore did the same, providing standardized tower block apartments as part of the national modernization strategy. Other cities, such as Toronto, have groves of suburban apartments, but Prague, Paris, and now Beijing have entire forests.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.888
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0090.006

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 head0.074
GPT teacher head0.302
Teacher spread0.228 · 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 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
Published2023
Admission routes1
Has abstractyes

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