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Record W2091045586 · doi:10.1016/j.soscij.2005.03.011

Updating the Bogardus social distance studies: a new national survey

2005· article· en· W2091045586 on OpenAlexaboutno aff
Vincent N. Parrillo, Christopher Donoghue

Bibliographic record

VenueThe Social Science Journal · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicElectoral Systems and Political Participation
Canadian institutionsnot available
Fundersnot available
KeywordsEthnic groupSocial distanceQuarter (Canadian coin)Scale (ratio)Race (biology)Social psychologyPsychologyTest (biology)SociologyDemographyGender studiesGeographyMedicine

Abstract

fetched live from OpenAlex

The last quarter of the 20th century witnessed a number of events and social transformations that have had great implications for religious and ethnic relations around the world. This study seeks to gauge the changes in sentiment towards various U.S. ethnic and religious groups by updating and replicating the Bogardus social distance scale. The Bogardus study, which was designed to measure the level of acceptance that Americans feel towards members of the most common ethnic groups in the United States, was conducted five times between 1920 and 1977 with very few changes in research design. Consistent with prior replications, the authors of this study collected a random sample of 2,916 college students and administered the social distance scale in the form of a questionnaire. The findings indicate that the mean level of social distance towards all ethnic groups, as well as the spread between the groups with the highest and lowest levels of social distance, decreased since 1977. Mean comparisons and ANOVA test also showed that gender, nation of origin, and race are all significant indicators of the level of social distance towards all 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)0.004
metaresearch head score (Gemma)0.009
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 score0.022
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.198
GPT teacher head0.484
Teacher spread0.287 · 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

Citations215
Published2005
Admission routes1
Has abstractyes

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