Bibliographic record
Abstract
With world champions breakers like Canada’s Karl ‘Dyzee’ Alba and the United Kingdom’s Ereson ‘Mouse’ Catipon, Filipino diaspora have been a global force in breaking; however the domestic Filipino dance scene has remained relatively understudied. In this article, I argue that breaking in the Philippines has been stymied by an ongoing ideological battle between professionalization and tradition. This juxtaposition is aptly distilled in the legacies of Dyzee and Mouse, in which the former mentored local dance professionals and the latter made the country a mecca for hip hop humanitarianism. These highly regarded thought-leaders each garnered local followings that are often at odds with each other. Despite these differences however, on the issue of the Olympics, the two camps have joined together in unprecedented fashion. In 2021, Filipino dance community leaders formed the B-boy & B-girl Association of the Philippines (BBAP) in order to unite the local scene and take ownership of the country’s Olympic endeavours. With Dyzee as president and Mouse as vice president, BBAP combines community-building and enterprise in order to enhance the Philippines’s global competitiveness and visibility in the world of breaking. This research aims to historicize Filipino breaking culture in light of the Olympic moment. To do so, it employs an ethnography among BBAP supported by personal and archival interviews from the 2011 documentary, Pinoy B-boy (Bitanga 2011).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.041 | 0.014 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".