Self-compassion in sport superhero team-ups present: Collaboration and innovation
Bibliographic record
Abstract
In 2003, self-compassion emerged in the general psychology literature; it has since exploded with research interest and attention by sport psychology trainees and faculty researchers. Self-compassion entails taking an adaptive approach to one's shortcomings, failures, and difficult experiences through self-kindness, common humanity, and mindfulness. Researchers have found that self-compassion has numerous benefits to sport performers, yet many athletes remain hesitant to embrace it. Given the growing popularity of self-compassion in sport research, critical questions must be asked, studied, and resolved to meaningfully advance the research area. Five faculty members, two postdoctoral fellows, and nine graduate student researchers working in the area of self-compassion have created superhero team-ups across five institutions to collaboratively address innovative topics about self-compassion in sport. The topics/questions covered by the duos, in 5-7 minutes each, include: Manly enough for self-compassion? (Ashley Kuchar University of Texas at Austin, Nathan Reis University of Saskatchewan [UofS]); TRAIN-EAT-MEDITATE-REPEAT (Jenna Gilchrist Pennsylvania State University, Olivia Chadwick UofS); Self-compassion...but at what age? (Leah Ferguson UofS, Autumn Nesdoly University of Alberta [UofA]); Duality of resilience (Eva Pila UofS, Kelsey Wright UofA); A stand for measurement (Amber Mosewich UofA, Margo Adam UofS); Sub-domains: A useful pursuit? (Tara-Leigh McHugh UofA, Abimbola Eke UofS); A sword of Damocles? (Catherine Sabiston University of Toronto, Danielle Cormier UofS); Self-compassion is coping (maybe) (Kent Kowalski UofS, Ben Sereda UofA). Leah Ferguson and Kent Kowalski will, respectively, introduce the symposium and respond to the topics covered prior to a 10-minute question and answer period with the audience and superhero panel.
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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 | 0.016 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".