MétaCan
Menu
Back to cohort

Sex Differences in Vocational Interests

2024· book-chapter· en· W4392022525 on OpenAlexaff
Julie Aitken Schermer, Kristi Baerg MacDonald

Bibliographic record

VenueOxford University Press eBooks · 2024
Typebook-chapter
Languageen
FieldSocial Sciences
TopicCareer Development and Diversity
Canadian institutionsWestern University
Fundersnot available
KeywordsVocational educationPsychologyPolitical sciencePedagogy

Abstract

fetched live from OpenAlex

Abstract In this chapter, we examine sex differences in vocational interests over generations and cohorts. Utilizing a large sample of adults (N = 1,774) who completed the Jackson Career Explorer (JCE), men scored significantly higher on the mathematics, physical science, engineering, adventure, dominant leadership, finance, sales, law, and professional advising interest scales. Women scored significantly higher on the creative arts, social science, personal service, teaching, social service, elementary education, family activity, and office work, interest scales as well as the work styles of stamina, accountability, and planfulness. To examine if sex differences in vocational interests have changed over time, sex differences for two editions of the Jackson Vocational Interest Survey manuals were compared. Surprisingly, few differences were found. In contrast, when the JCE responses were analyzed by age cohorts, sex differences do appear to be slightly smaller in the younger sample, suggesting that further analyses are required with successive generations.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.013
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0130.001

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 head0.053
GPT teacher head0.240
Teacher spread0.187 · 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
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueOxford University Press eBooksSame topicCareer Development and DiversityFrench-language works237,207