Postdoctoral Fellowships in Physical Education and Sport Sciences/Kinesiology: An International Investigation of Structures and Experiences
Bibliographic record
Abstract
Postdoctoral fellowships (PDFs) are becoming increasingly common yet remain nebulous to many. The purpose of this research has been to investigate the structures and experiences of PDFs for fellows and supervisors in physical education and sport sciences/kinesiology (PESSK) internationally. Fourteen fellows and five supervisors participated in one-to-one semi-structured interviews. Data were thematically analyzed and interpreted through Dewey’s theory of experience. Findings revealed participants understood PDFs in relation to doctoral education and as preparation for academic careers. Reasons for engaging included ambitions for new contexts and mentorship, unprepared or unable to secure faculty employment, and facilitating professional learning. Challenges related to low salaries, family considerations, structural and institutional obstacles, international barriers, and high turnover. Perceived values included professional and personal growth and fellows as a learning, relationship, and productivity resource for supervisors. This research holds important implications for postdocs, supervisors, institutions, and funding agencies in supporting early-career scholarship in PESSK.
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.007 |
| 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".