MétaCan
Menu
Back to cohort
Record W1530839640 · doi:10.1177/1078087415596241

The Formation of Business Improvement Districts in Low-Income Immigrant Neighborhoods of Los Angeles

2015· article· en· W1530839640 on OpenAlexaboutno aff
Wonhyung Lee

Bibliographic record

VenueUrban Affairs Review · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicUrban, Neighborhood, and Segregation Studies
Canadian institutionsnot available
FundersHorace H. Rackham School of Graduate Studies, University of Michigan
KeywordsImmigrationQuarter (Canadian coin)Distribution (mathematics)MulticulturalismEconomic growthCommunity organizationPolitical scienceBusinessGeographyEconomicsArchaeology

Abstract

fetched live from OpenAlex

Business improvement districts (BIDs) are local organizations that have been revitalizing commercial areas for the last two decades in the United States. However, not every commercial district has succeeded in establishing BIDs despite some initial efforts. This research presents a comparative examination of two neighborhoods in Los Angeles—MacArthur Park and the Byzantine Latino Quarter (BLQ)—to examine the BID formation process in poor immigrant neighborhoods and to identify how community characteristics differ between the neighborhood that succeeded in BID formation and the other that did not. The BLQ displayed distinguishable factors that may have contributed to successful BID formation, including invested community stakeholders, organizational resources, residents’ activism, and efforts to embrace multiethnic groups. This research demonstrates that community organizing capacity and characteristics can change the course and outcome of BID formation. This study also offers insights for multicultural community organizing and equitable distribution of public services to the areas with inconclusive or ineffective efforts of BID formation.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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: none
Teacher disagreement score0.914
Threshold uncertainty score0.369

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.030
GPT teacher head0.284
Teacher spread0.255 · 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 teacher head, 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

Citations9
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueUrban Affairs ReviewSame topicUrban, Neighborhood, and Segregation StudiesFrench-language works237,207