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Record W2084418590 · doi:10.1037/0022-3514.94.5.913

Societal threat, authoritarianism, conservatism, and U.S. state death penalty sentencing (1977-2004).

2008· article· en· W2084418590 on OpenAlexaff
Stewart J. H. McCann

Bibliographic record

VenueJournal of Personality and Social Psychology · 2008
Typearticle
Languageen
FieldPsychology
TopicDeath Anxiety and Social Exclusion
Canadian institutionsCape Breton University
Fundersnot available
KeywordsConservatismAuthoritarianismIdeologyPopulationLiberalismFundamentalismState (computer science)Context (archaeology)Political scienceCriminologyPoliticsDemocracyPsychologySociologyLawDemographyGeography

Abstract

fetched live from OpenAlex

On the basis of K. Stenner's (2005) authoritarian dynamic theory, it was hypothesized that the number of death sentences and executions would be higher in more threatened conservative states than in less threatened conservative states, and would be lower in more threatened liberal states than in less threatened liberal states. Threat was based on state homicide rate, violent crime rate, and non-White percentage of population. Conservatism was based on state voter ideological identification, Democratic and Republican Party elite liberalism-conservatism, policy liberalism-conservatism, religious fundamentalism, degree of economic freedom, and 2004 presidential election results. For 1977-2004, with controls for state population and years with a death penalty provision, the interactive hypothesis received consistent support using the state conservatism composite and voter ideological identification alone. As well, state conservatism was related to death penalties and executions, but state threat was not. The temporal stability of the findings was demonstrated with a split-half internal replication using the periods 1977-1990 and 1991-2004. The interactive hypothesis and the results also are discussed in the context of other threat-authoritarianism theories and terror management theory.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.635
Threshold uncertainty score0.910

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.084
GPT teacher head0.369
Teacher spread0.285 · 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 teacher head, 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

Citations112
Published2008
Admission routes1
Has abstractyes

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