Does the Big Five Explain Envy? ‘True’ Correlations and Associations with Age, Sex, Education, and Income in Multi-rater Data
Bibliographic record
Abstract
Most of the literature has examined envy using predominantly self-report measures. In the current study, we combined self- and informant-reports to estimate personality domains’ (Big Five, Dark Dyad) ‘true’ correlations (rtrues) with benign and malicious envy using three samples tested in different languages (Estonian, N = 20,925; Russian, N = 767; English, N = 593). This allowed us to control for measurement- and occasion-specific biases, as well as random error. We assessed how the two types of envy relate to age, sex, education, and income (both personal and household). Additionally, we conducted linear regressions on self-reports (Estonian, N = 62,545; Russian, N = 3025; English, N = 640), controlling for the Big Five and demographic variables. Big Five domains’ rtrues with benign (-.65 to .21; -.69 to .37; -.76 to .08) and malicious envy (-.40 to .26; -.47 to .30; -.49 to .14) replicated across the Estonian-, Russian-, and English-speaking samples, respectively. Across the three samples, we found benign and malicious envy to be negatively associated with age (in years) and sex (female). In the Estonian-speaking sample, those with higher levels of benign and malicious envy tended to report higher personal and household incomes. Our findings show that envy is not redundant with the Big Five personality domains. We also highlight the importance of incorporating and collecting data from multiple raters in personality studies, especially for socially transgressive traits.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".