On the nature, predictors, and outcomes of work passion profiles: A comparative study across samples of Indigenous and non-Indigenous Australian employees
Bibliographic record
Abstract
Based on the dualistic model of passion, we conducted person-centered analyses to assess how harmonious and obsessive passion for work combine within distinct profiles of employees and document the associations between these profiles and theoretically relevant predictors and outcomes. We also investigate whether the nature of these profiles, and their associations with predictors and outcomes, differs between samples of Australian Indigenous ( N = 591; 66.0% female, M age = 41.87) and non-Indigenous ( N = 605; 56.0% female, M age = 44.79) employees. Our results uncovered four profiles, which were replicated across both samples of employees: Harmonious Passion Dominant, Obsessive Passion Dominant, Mixed Passion-Obsessive Passion Dominant, and Low Passion. Role ambiguity and job overload were found to be related to employees’ likelihood of profile membership in a way that was similar across both samples. Finally, psychological well-being and resilience at work differed as a function of profile membership in a way that was replicated across samples. In addition to the theoretical implications for research on work passion, these results clearly highlight how work passion has highly similar implications for Indigenous and non-Indigenous Australian employees. JEL Classification: I3 Welfare, Well-Being, and Poverty
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".