MétaCan
Menu
Back to cohort
Record W2013108856 · doi:10.1177/0049124105280198

Mapping Social Distance

2005· article· en· W2013108856 on OpenAlexaboutno aff
Michael J. White, Ann H. Kim, Jennifer E. Glick

Bibliographic record

VenueSociological Methods & Research · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicUrban, Neighborhood, and Segregation Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMultidimensional scalingEthnic groupCensusMetropolitan areaImmigrationDiversity (politics)GeographySocial distanceSociologySocial groupRacial diversityCensus tractCultural diversityGeographical distanceEconomic geographyRegional scienceDemographyDemographic economicsSocial scienceStatisticsMathematicsAnthropologyPopulation

Abstract

fetched live from OpenAlex

The increasing diversity of immigrant-receiving countries calls for measures of residential segregation that extend beyond the conventional two-group approach. The authors represent simultaneously the relative social distance occupied by a wide array of ethnic groups. They use census tract tabulations for the Toronto Consolidated Metropolitan Area in 1996 and the technique of multidimensional scaling to summarize the residential neighborhood pattern of the city’s largest 50 ethnic groups. From the two-dimensional multidimensional scaling configuration, the authors find that African/Caribbean groups and blacks were highly clustered and shared common patterns of segregation with other groups. This study highlights the value of looking beyond broad racial or panethnic classifications in understanding ethnic congregation and residential segregation patterns. The results also demonstrate the merits of this method in providing a more conceptually meaningful way to understand social distance among groups.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0070.006
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.002

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 head0.496
GPT teacher head0.608
Teacher spread0.112 · 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 designSimulation or modeling
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

Citations41
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueSociological Methods & ResearchSame topicUrban, Neighborhood, and Segregation StudiesFrench-language works237,207