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Southern Distinctiveness over Time, 1972-2000

2002· article· en· W2225797527 on OpenAlexaboutno aff
Tom W. Rice, William P. McLean, Amy J. Larsen

Bibliographic record

VenueAmerican Review of Politics · 2002
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Capital and Networks
Canadian institutionsnot available
Fundersnot available
KeywordsOptimal distinctiveness theoryWhite (mutation)Quarter (Canadian coin)UrbanityRace (biology)HistoryGender studiesPolitical scienceSociologyPsychologySocial psychology

Abstract

fetched live from OpenAlex

Scholars have long been interested in the cultural differences between the southern United States and the rest of the nation. In this study we update and extend earlier work in this area by comparing and tracking the responses of southerners and non-southerners to over 75 questions from the 1972-2000 cumulative General Social Surveys. The analyses generate four conclusions. First, the attitudes and behaviors of southerners are more conservative than those of non-southerners in many areas, including race, gender, religion, sex, social capital, and tolerance. Second, the magnitude of these regional differences remains about the same regardless of whether we compare all southerners and non-southerners or just white southerners and non-southerners. This suggests that Southern culture is not just a “white” southern culture as many scholars have argued in the past. Third, the differences between southerners and non-southerners persist, although often to a lesser degree, after controlling for structural variables such as education, income, and urbanity. The implication is that southern distinctiveness is a product of both deep-seeded cultural differences and structural differences between regions. Fourth, there is very little evidence that regional differences have declined over the past quarter century, challenging those who contend that southern culture is in retreat.

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)0.002
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 score0.051
Threshold uncertainty score0.102

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.001
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 head0.016
GPT teacher head0.300
Teacher spread0.284 · 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

Citations20
Published2002
Admission routes1
Has abstractyes

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