Bibliographic record
Abstract
ABSTRACT Recent research (Vallerand et al., 2003) has supported the existence of two types of passion for activities: a harmonious and an ob-sessive passion. The purpose of this investigation was to study the processes likely to lead to the development of passion. Three studies using correla-tional and short-term longitudinal designs with varied populations ranging from beginners to experts reveal that identification with the activity, activity specialization, parents ’ activity valuation, and autonomy support predict the development of passion. Furthermore, results show that children and teenagers whose environment supports their autonomy are more likely to develop a harmonious passion than an obsessive one. Conversely, children and teenagers who highly value activity specialization, who rely heavily on their activity for self-definition, and whose parents highly value the activity are more likely to develop an obsessive passion. This research was facilitated by a doctoral fellowship from the Social Sciences and Humanities Research Council of Canada (SSHRC) to the first author and funded by grants from SSHRC and the Fonds de Recherche sur la Sociéte ́ et la Culture (FQRSC) to the second, sixth, and seventh authors. Correspondence concerning this article should be addressed to Geneviève A. Mag-
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.516 | 0.102 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".