Understanding Flourishing in Doctoral Studies: Exploring the Positive Experiences of Mature Learners Through Appreciative Inquiry
Bibliographic record
Abstract
The number of mature students in PhD programs in Canada has increased over recent years. While research suggests older adults are more intrinsically motivated and tend to academically outperform their younger peers, studies generally focus on the problems and barriers mature students frequently encounter. The purpose of this research is to fill a gap in the literature by investigating the positive experiences of four diverse, nontraditional PhD students at a Canadian university. Using a framework of flourishing rooted in Self-Determination Theory and Appreciative Inquiry methodology, our findings contribute to the literature by extending the definition of flourishing, describing its affective dimensions, and its fluctuations. We also draw attention to the critical need for strong institutional supports to promote the flourishing of mature doctoral learners. By deepening our understanding of older nontraditional doctoral students’ positive experiences, institutions will be better equipped to meet the needs of this growing demographic and create more equitable conditions that will facilitate their ability to flourish and make valuable contributions to the academy.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.004 |
| 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".