A Case of Mistaken Identity? Latent Profiles in Vocational Interests
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Vocational interest surveys have traditionally employed a typology (i.e., the Realistic, Investigative, Artistic, Social, Enterprising, and Conventional [RIASEC] model) to distinguish individuals. Within this framework, respondents are identified as representing various types of people based on their interests in work-related activities. However, much of the existing literature on vocational interest testing has focused almost exclusively on traditional variable-centered approaches to understanding the nomological network around vocational interest variables. Therefore, the focus of the current article is an application of a person-centered approach, latent profile analysis (LPA). Using LPA, we found evidence of eight qualitatively and quantitatively distinct subgroups or types of individuals differentiated on the basis of interests in the RIASEC variables. Further, across the five-factor model and Dark Triad personality variables, minor, yet theoretically sound, differences across the eight vocational interest subgroups were found. Theoretical and practical implications are discussed.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it