Bibliographic record
Abstract
My major research paper (MRP) seeks to explore the relationship between resilience strategies and women in martial arts. It is the foundation for a knowledge translation project which seeks to create an interactive fiction piece to teach self-motivation strategies utilized by women athletes in combat arts. This interactive fiction piece is not meant to be a clinical tool for depression or anxiety, but is instead a self-empowerment tool. By interviewing 10 women who participate in Muay Thai as amateur fighters, I was able to evaluate which strategies were common and effective. These included discipline, organization, growth mindset, and finding an overarching purpose. There were also many barriers that were similar between the participants of my study, including ineffective coaching techniques, unsupportive friends and family, and unrealistic representations of women in martial arts by the media. Lastly, the participants of my study offered several suggestions for the game creation, including designing accurate physical representations of women's bodies, acknowledging barriers and sacrifices for women in the sport, and offering the opportunity in-game for self-reflection self-reflection to mimic self-improvement. The term knowledge translation reveals that we speak in different languages and conventions than people outside of academia. The term knowledge mobilization illustrates that we have to go out of our way to reach these people, many of whom are the subjects of our research. These two concepts can and should be integrated as we conduct our research, write our papers, and publish; they should not simply exist as an afterthought. I urge every scholar who reads this paper to consider the ways we can create a culture which encourages inclusivity and equitable access.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".