Passion for science and the pursuit of scientific studies: The mediating role of rigid and flexible persistence and activity involvement
Bibliographic record
Abstract
This paper presents a new perspective on persistence and how it relates to passion and outcomes in the field of education. Based on the Dualistic Model of Passion (Vallerand, 2015), we propose the existence of two types of persistence – flexible and rigid that are specific to the activity one is passionate about. Flexible persistence takes origin in harmonious passion and entails pursuing activity goals with an open and broad focus that allows reaching the desired activity goals and outcomes while also attaining other life outcomes. Conversely, rigid persistence takes root mostly in obsessive passion and entails pursuing activity goals with a narrower focus that facilitates reaching some activity goals and outcomes, but not other benefits in life, generally. Results from two cross-sectional studies conducted with postsecondary science students (Study 1, N = 591 and Study 2, N = 198) provided support for this new perspective on persistence. Both types of persistence mediated the relationship between passion for science and academic performance (Studies 1 and 2), which in turn led to intentions to pursue in a scientific field at university (Study 1) and actual applications in STEM (Science, Technology, Engineering, & Mathematics) university programs (Study 2). Additionally, students who persisted flexibly also experienced positive outcomes outside of the school environment in both studies. Theoretical and practical implications of academic persistence are discussed within the perspective of the DMP.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".