The role of self‐esteem contingencies in the distinction between obsessive and harmonious passion
Bibliographic record
Abstract
Abstract The Dualistic Model of Passion (Vallerand et al., 2003) shows that people can experience a harmonious or an obsessive passion toward an activity. Mageau and Vallerand (2007; Mageau et al., 2009) have argued that self‐related processes, such as contingencies of self‐worth, are central in the distinction between the two types of passion. Specifically, it was proposed that people with an obsessive passion rely more heavily on their passionate activity to derive self‐esteem than people with a harmonious passion such that they should experience self‐esteem fluctuations as a function of their performances in their passionate activity. This study tested this hypothesis. Using self‐reports, results first showed that the more people have an obsessive passion the more they report experiencing self‐esteem fluctuations that covary with their performances in their passionate activity. In contrast, people with a harmonious passion did not report experiencing more, or less, self‐esteem fluctuations. Second, hierarchical linear modeling confirmed that, in a real‐life setting, the more people report an obsessive passion toward a card game, the greater is the impact of performance on their state self‐esteem. Taken together, these findings suggest that obsessive, but not harmonious, passion triggers contingencies between people's self‐esteem and their passionate activity. Copyright © 2011 John Wiley & Sons, Ltd.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".