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Record W4226026445 · doi:10.31234/osf.io/vksc2

The impact of trait introversion-extraversion and identity on state authenticity: Debating the benefits of extraversion

2022· preprint· en· W4226026445 on OpenAlexaff
Isabella Bossom, John M. Zelenski

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsCarleton University
Fundersnot available
KeywordsExtraversion and introversionTraitSocial psychologyPsychologyAffect (linguistics)Identity (music)PersonalityBig Five personality traitsValue (mathematics)Trait theoryMathematicsAestheticsCommunication

Abstract

fetched live from OpenAlex

Research suggests that trait introverts feel more authentic when acting extraverted. We explored boundaries of this idea by assessing trait and identities as introvert or extravert and asking participants to debate extraversion’s value. Students (Study 1: N = 310, Study 2 direct replication: N = 407) were randomly assigned to pro or con sides in the debate and then reported their state authenticity and affect. Results suggested interactions between individual differences (trait, identity) and debate condition on authenticity. Counter-dispositional and counter-identity debating decreased authenticity, though with variation in strength across studies. Affect did not follow this pattern. These findings provide preliminary evidence for the importance of trait-related identities and suggest limits to the benefits of embracing counter-dispositional extraversion.

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)0.003
metaresearch head score (Gemma)0.014
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 score0.003
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.033
GPT teacher head0.370
Teacher spread0.337 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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