A case study of commitment and compensation: Examining a 52 year old Masters athlete
Bibliographic record
Abstract
Masters athletes are considered models of successful aging (Dionigi & Horton, 2010), who invest vastly in training and competition, and have a wealth of experiences in sport/physical activity (Young & Medic, 2011). Understanding factors that facilitate masters athletes’ commitment may provide other adults with adaptive strategies for remaining active (Langley & Knight, 1999). In this case study, we interviewed a male masters athlete (Andrew: aged 52, nationally ranked runner, provincially ranked squash player, and regional winner of cross country skiing and orienteering championships) about personal and social conditions facilitating sport commitment and strategies used to maintain elite performance. Andrew’s accounts were analyzed deductively (Joffe & Yardley, 2003) using the Sport Commitment Model (SCM; Scanlan et al., 2003) and the Model of Selective Optimization with Compensation (MSOC; Baltes & Baltes, 1990). With respect to SCM, Andrew committed to sport because he inherently enjoyed training and competing, benefitted from social connections around sport in the absence of social pressures, and was further afforded opportunities to compete and test him-self, to travel to new places, and to feel youthful. With respect to MSOC, Andrew sustained year-round activity by prioritizing his sports and reducing his participation intensity in low ranked activities before major competitions in other activities. Moreover, he increased his sport-specific practice, and used his knowledge and experience to alter his techniques and training to compensate for age-related losses. Results show support for using the aforementioned models to understand why masters athletes remain committed and how they continuously achieve high levels of success.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".