From Many Masters to Many Students: YouTube, Brazilian Jiu Jitsu, and communities of practice
Bibliographic record
Abstract
Prior to the last two decades, martial arts practice in Western countries was shrouded by secrecy and esoteric philosophies and there was very little transmission of techniques between practitioners outside of individual martial arts clubs. Techniques were passed down from one master to many students. With the increase in popularity of mixed martial arts, there has been a greater exposure to and transmission of martial arts techniques between practitioners across the globe. Now, anyone with access to television or the Internet can watch and analyze martial arts techniques. In relation to one prominent martial art, Brazilian Jiu Jitsu, the Internet has come to serve as the means by which practitioners transmit Brazilian Jiu Jitsu techniques, profess the philosophies of Brazilian Jiu Jitsu, and reflect on the current state of the art. Based on reflections on the last five years of Brazilian Jiu Jitsu practice and analysis of video posts by martial arts practitioners and comments by viewers, this article probes the nexus between ‘offline’ communities of practice and ‘virtual’ communities of practice that are centered on Brazilian Jiu Jitsu technique acquisition. Specifically, focus is on the use of YouTube as a tool for disseminating and learning Brazilian Jiu Jitsu techniques. Drawing from theories of communities of practice and skill acquisition, this article examines how the exhibition of Brazilian Jiu Jitsu techniques on YouTube has become integrated into practice of Brazilian Jiu Jitsu across the globe. With this mediatization of Brazilian Jiu Jitsu and other martial arts, it is no longer viable to conceive of pure internet-based and offline social networks. The transmission of martial art techniques now consists of many masters and many students.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".