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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".