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Record W1973447766 · doi:10.1017/s0140525x13002744

Confounding valence and arousal: What really underlies political orientation?

2014· letter· en· W1973447766 on OpenAlexaff
Shona M. Tritt, Michael Inzlicht, Jordan B. Peterson

Bibliographic record

VenueBehavioral and Brain Sciences · 2014
Typeletter
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
Fundersnot available
KeywordsArousalValence (chemistry)Biology and political orientationPoliticsPsychologySocial psychologyConfoundingCognitive psychologyOrientation (vector space)Political scienceMedicineChemistryLawMathematicsGeometry

Abstract

fetched live from OpenAlex

The negative valence model of political orientation proposed by Hibbing et al. is comprehensive and thought-provoking. We agree that there is compelling research linking threat to conservative political beliefs. However, we propose that further research is needed before it can be concluded that negative valence, rather than arousal more generally, underlies the psychological motivations to endorse conservative political belief.

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)0.006
metaresearch head score (Gemma)0.037
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: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.014
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.037
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.005
Scholarly communication0.0020.003
Open science0.0020.001
Research integrity0.0140.013
Insufficient payload (model declined to judge)0.0050.002

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 head0.109
GPT teacher head0.436
Teacher spread0.326 · 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
GenreCommentary

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

Citations17
Published2014
Admission routes1
Has abstractyes

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