The Benefits of an Inclusive Karate Program on an Individual’s Well-Being
Bibliographic record
Abstract
Happiness and longevity, including a desire to reach 100 years old and earn the title of “centenarian,” are common goals for many (Ekerdt et al., 2017). In Blue zones, or geographic areas with longer life expectancies, these goals often become a reality, with a greater number of centenarians observed than in other regions. Okinawa, Japan, for example, has a centenarian prevalence of 7234 per 100,000, exceeding the national average by 30% (National Institute of Population and Social Security Research [IPSS], 2022; Human Mortality Database [HMD], 2021). In other developed countries such as Canada, where cultural factors promote sedentary lifestyle habits, the centenarian population is reduced by approximately half to 3664 per 100,000 (HMD, 2021). To combat this, the implementation of biohacks from regions such as Okinawa, where longevity is linked to social health and light exercise, may prove to be beneficial (Inoue et al., 2009; Kavedžija, 2015; Yamada et al., 2012). These biological strategies, implemented on a “do-it-yourself” basis, ultimately aim to improve quality of life and well-being (Meyer, 2020). Karate, an accessible Japanese exercise overcoming constraints such as poor infrastructure or access to healthy foods, is one such approach, ultimately supporting participants by promoting exercise and social connection (Inoue et al., 2009).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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".