The Knowledge‐Experience‐Evaluation Relationship: A Structural Equations Modeling Test of Gender Differences
Bibliographic record
Abstract
Abstract This study examines the differences between males and females concerning the relationship of subjective knowledge (SK), experience (EXP), and perceived product evaluation difficulty (DE). Using survey data, we test structural equation models (SEM) of the relationships between these three variables, in the context of four product categories. We verify, via confirmatory factor analysis, that EXP and SK are separate yet related constructs. We then test separate (m/f) SEM models, followed by multi‐group model analysis. A number of significant gender differences are revealed. For males, SK fully mediates the relationship between EXP and DE, whereas for females the latter is both directly and indirectly (via SK) related to EXP. Females' DE scores are higher than males in most of the product categories considered. Other observed gender differences, implications, and future research directions are discussed. Résumé Cette recherche examine les différences entre les hommes et les femmes concemant la relation entre la connaissance subjective (SK), l'expérience (EXP), et la difficulté d'évaluer dans la perception du produit (DE). Nous nous servons de données recueillies au cours d'une enquête pour tester les modèles d'équations structurelles des relations entre ces trois variables, et ce pour quatre catégories de produits. Grâce à l'analyse factorielle confirmatoire, nous montrons que l'EXP et la SK sont des concepts distincts mais corrélés. Notre étude nous permet de relever des différences notoires entre les hommes et les femmes. Pour les hommes, la relation entre l'EXP et la DE se fait par l'intermédiaire de la SK. Pour les femmes, la DE est reliée directement et indirectement (via SK) à l'EXP. Pour la plupart des catégories étudiées. la moyenne de la variable DE des femmes est supérieure à celle des hommes. L'article se penche également sur d'autres différences entre les hommes et les femmes, sur les conséquences de ces différences, et propose des pistes de recherche futures dans le domaine.
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How this classification was reachedexpand
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".