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Record W4406103344 · doi:10.1177/00332941241311497

Associations Between Vocational Interests and Personality “Beyond” the Big-Five

2025· article· en· W4406103344 on OpenAlexaff
Sereena Dargan, Julie Aitken Schermer

Bibliographic record

VenuePsychological Reports · 2025
Typearticle
Languageen
FieldPsychology
TopicPersonality Traits and Psychology
Canadian institutionsWestern University
Fundersnot available
KeywordsVocational educationPsychologyPersonalityBig Five personality traitsSocial psychologyPersonality Assessment InventoryClinical psychologyDevelopmental psychologyPedagogy

Abstract

fetched live from OpenAlex

This investigation explores the relationships between vocational interests and personality dimensions suggested to be “beyond” the Big Five or Five Factor Model. Participants (653 adults; 125 men and 528 women, with a mean age of 40.57 years, SD = 16.61, range 18–92) provided data on the 10 personality dimensions of the Supernumerary Personality Inventory (SPI; Paunonen, 2002), along with the 34 scales (27 work interests and seven work style preference scales) of the Jackson Career Inventory. Several significant associations, such as negative correlations between conventionality and an interest in the arts, negative correlations between femininity and an interest in science, positive correlations between humorous and an interest in nature/adventure/medicine, and positive correlations between manipulativeness and an interest in business were identified bringing to light new perspectives on how personality relates to different occupational roles and styles. The findings stress the importance of employing diverse personality and vocational interest measures to provide a more holistic view of associations between personality and career preferences. Limitations and future research are discussed.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.240
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.088
GPT teacher head0.423
Teacher spread0.334 · 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.

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
Published2025
Admission routes1
Has abstractyes

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