MétaCan
Menu
Back to cohort
Record W2093299728 · doi:10.1353/dem.2006.0020

Friends for better or for worse: Interracial friendship in the United States as seen through wedding party photos

2006· article· en· W2093299728 on OpenAlexaff
Brent Berry

Bibliographic record

VenueDemography · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFriendshipPolitical scienceGender studiesPsychologyGeographySociologyDemographySocial psychology

Abstract

fetched live from OpenAlex

Friendship patterns are instrumental for testing important hypotheses about assimilation processes and group boundaries. Wedding photos provide an opportunity to directly observe a realistic representation of close interracial friendships and race relations. An analysis of 1,135 wedding party photos and related information shows that whites are especially unlikely to have black friends who are close enough to be in their wedding party. Adjusting for group size, whites and East and Southeast Asians (hereafter E/SE Asians) are equally likely to be in each other's weddings, but whites invite blacks to be in their wedding parties only half as much as blacks invite whites, and E/SE Asians invite blacks only one-fifth as much as blacks invite E/SE Asians. In interracial marriages, both E/SE Asian and black spouses in marriages to whites are significantly less likely than their white spouses to have close friendships with members of their spouse's race.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.062
Threshold uncertainty score0.123

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 studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.041
GPT teacher head0.335
Teacher spread0.294 · 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

Citations38
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueDemographySame topicMigration, Ethnicity, and EconomyFrench-language works237,207