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Record W7005459844

Racial Segregation in the Rise and Fall of 22nd Street South: The Unfolding Story of the Historic Black Business Recreational District in St. Petersburg, Florida

2017· article· en· W7005459844 on OpenAlexaff

Bibliographic record

VenueScholarship@Western (Western University) · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicDiverse Scientific and Economic Studies
Canadian institutionsWestern University
Fundersnot available
KeywordsLegislationCharterWhite (mutation)DemiseGovernment (linguistics)Recreation
DOInot available

Abstract

fetched live from OpenAlex

A clause entitled “Segregation of Races” was inserted in the St. Petersburg City Charter in 1931. It wasn’t until 1936, however, that the clause gave rise to the first segregated housing zone within the city. In this report we provide evidence to suggest that it was the Federal Government and not the St. Petersburg city council, as has been claimed, that was responsible for the implementation of this clause and the segregated commercial district that developed along 22nd Street South. We then document the rise of this commercial district and present further evidence that city council showed little interest in preventing white store owners from operating businesses in the district long past the time when the segregation clause should have prevented them from doing so. Finally, we examine the reasons for the demise of the district in light of federal legislation that banned segregation in the 1960s, and the suggestion that highway I-275 may also have played a role in contributing to this demise.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.361
Threshold uncertainty score0.718

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0180.005
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0050.000

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 head0.112
GPT teacher head0.259
Teacher spread0.147 · 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
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
Published2017
Admission routes1
Has abstractyes

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