North American Society for the Psychology of Sport and Physical Activity Annual Conference, June 3–5, 2025
Bibliographic record
Abstract
Disciplines within and related to Kinesiology have the formidable task and unique opportunity to move the world by motivating people's psyche and transporting their minds and bodies to new heights and dimensions of optimal performance.Although laudable, this task/opportunity is not one that is culture-neutral, given the ever-increasing cultural diversity of the personhood of Kinesiology constituents and their culturally infused and culturally informed expectations and aspirations.Markus & Kitayama (1998) noted long ago that a person is a social and collective construction made possible through participation and interaction with salient practices and symbolic meanings of a given culture.Kanagawa et al. (2001) also opined that cultural contexts influence the universe of self-conceptions from which a person's working self-concept is constituted.How we see ourselves shapes our lives and is shaped by our culture, because the self is mapped in units of culture that impacts our ways knowing, interpreting, doing, and behavingand this includes behaviors in and related to physical activity.Herein lies the overall focus of my presentation.During this keynote, I will address theoretical and practical implications of self-culture dynamics on the mind-body movement phenomenon.In doing so, I will center the concept of a 'cultured' self as the seer and that which is seen, and as the actor and that which is acted uponwhile also noting the implications for motivation, presentation, and optimal performance during physical activity.Notwithstanding my mindfulness of the sociopolitical challenges and ever-changing 'rules of the game' for addressing culture and the consequent disparities, injustices, and marginalization for which it and its ancillaries are often the subject and object, I believe that Kinesiology's motor development, motor learning and control, and sport and exercise psychology professionals must 'adapt to the rules' and 'stay in the game!' As such, I will conclude this presentation with a discussion of the imperative of meeting the moment at the fore with vigilance and intentionality to: (a) empower the self of the culturally diverse array of constituents, and (b) elevate the cultural impact of the profession(s).
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.443 | 0.200 |
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".