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Record W4256307045 · doi:10.3138/9781487531867-fm

Frontmatter

2019· book-chapter· en· W4256307045 on OpenAlexaffabout
K. Murat Güney, Roger Keil, Murat Üçoğlu, K Murat, Murat Üco�lu

Bibliographic record

VenueUniversity of Toronto Press eBooks · 2019
Typebook-chapter
Languageen
FieldSocial Sciences
TopicUrbanization and City Planning
Canadian institutionsYork University
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

Providing a systematic overview of large-scale housing projects, Massive Suburbanization investigates the building and rebuilding of urban peripheries on a global scale.Conceptual and empirical chapters revisit classic cases of large-scale suburban building in Canada, the United States, France, Germany, and the former Czechoslovakia, and examine new peripheral estates in China, Egypt, Israel, Morocco, South Africa, Turkey, and the Philippines.Offering a universal inter-referencing point for research on the dynamics of "massive suburbia," this book builds a new discussion pertaining to the problems of the urban periphery, urbanization, and the neoliberal production of space.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.220
Threshold uncertainty score0.314

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.7800.621

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 head0.221
Teacher spread0.195 · 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; the direct Gemma label and the distilled Codex classifier 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
Published2019
Admission routes2
Has abstractyes

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