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Record W1979435359 · doi:10.2747/0272-3638.31.2.228

Geographies of Resilient Social Networks: The Role of African American Barbershops

2011· article· en· W1979435359 on OpenAlexaff
Patricia Burke Wood, Rod K. Brunson

Bibliographic record

VenueUrban Geography · 2011
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsYork University
Fundersnot available
KeywordsMetropolitan areaEthnographyInstitutionPsychological resilienceSociologyFace (sociological concept)GeographyGender studiesSocioeconomicsAnthropologySocial scienceArchaeology

Abstract

fetched live from OpenAlex

This study examines the geographic reach of the African American barbershop, a neighborhood institution that is commonly acknowledged as important, yet whose significance is often overlooked. Data were gathered regarding the residential location of clienteles for two barbershops in the inner suburbs of St. Louis, Missouri, a city with a history of racial segregation. Plotting this information on maps of the area revealed that many customers travel to these shops from well outside the neighborhood. The significance of the spatial extent of the shops' communities is contextualized with in-depth interviews and ethnographic research on life inside the businesses. In light of urban and inner-suburban decline in metropolitan St. Louis, this research strengthens the case for the importance of such informal institutions for understanding the resilience of the African American community in the face of the reproduction of racialized social geographies.

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)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0060.005
Scholarly communication0.0030.003
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.022
GPT teacher head0.302
Teacher spread0.280 · 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 designQualitative
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

Citations18
Published2011
Admission routes1
Has abstractyes

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