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Record W7062812446

Urban Renewal North and South: The Case of São Paulo and New York During and After WWII

2014· other· en· W7062812446 on OpenAlexaboutno aff

Bibliographic record

VenueIssue Lab (Candid) · 2014
Typeother
Languageen
FieldPhysics and Astronomy
TopicParticle Detector Development and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsLatin AmericansSurpriseWorld War IICenter (category theory)Quarter (Canadian coin)Presidential addressPresidential systemGeorge (robot)
DOInot available

Abstract

fetched live from OpenAlex

When Nelson Rockefeller arrived at the São Paulo airport on June 18, 1969, as the head of Richard Nixon's Presidential Mission to Latin America, he delivered a statement that must have thrilled his paulistano hosts -- especially those who looked to New York as a model city. In addition to calling São Paulo Latin America's most modern industrial center and the world's fastest growing city, among other superlatives associated with the city at the time, Rockefeller went on to say that the usual comparisons between São Paulo and Chicago were now "out-ofdate." For Rockefeller, the more accurate parallel was between São Paulo and his own New York. Though unusual, the fact that Nelson Rockefeller emphasized the similarities between São Paulo and New York should come as no surprise given that he, and a group of influential politicians, engineers, city planners, architects, and museum directors from both New York and São Paulo, had been working in concert to improve US-Brazil relations and bring the two cities closer together since the early 1940s.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.588
Threshold uncertainty score0.829

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0230.008
Scholarly communication0.0060.003
Open science0.0010.006
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0070.000

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 head0.008
GPT teacher head0.206
Teacher spread0.198 · 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 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

Citations0
Published2014
Admission routes1
Has abstractyes

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