Passion for Science and the Pursuit of a Scientific Domain: The Mediating Role of Persistence and Activity Engagement
Bibliographic record
Abstract
The current research is derived from the Dualistic Model of Passion positing the existence of a harmonious (HP) and obsessive passion (OP). We propose and test a serial mediation model of educational persistence in two cross-sectional studies with first (N = 366) and second-year (N = 199) science students in college with the main hypothesis that OP and HP will induce different types of persistence. In both studies, we hypothesized that OP would positively predict a rigid persistence, whereas HP would positively predict flexible persistence and that only those who persisted flexibly would also experience wellbeing outside of school. We also expected both types of persistence to mediate the relationship between passion for science and self-reported science grades, which in turn would lead to intentions to pursue science at university (Study 1) and future applications in STEM university programs (Study 2). Furthermore, we assumed that extracurricular scientific activities would also be positively associated with academic attainment and the pursuit of sciences in higher education (Study 2). Our hypotheses were fully supported. Additionally, only students with a HP engaged in extracurricular scientific activities which, in turn, provided a stronger prediction of science grades compared to rigid and flexible persistence.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".