Bibliographic record
Abstract
Abstract Martial Sound is an ethnographic book examining the music of traditional Chinese martial arts. More specifically, the book investigates the gong and drum percussion used to accompany the lion dance and kung fu, as practised by the Hong Luck Kung Fu Club in Toronto, Canada. Hong Luck’s history and character are distinctive, but the club’s practices and approaches are typical of many styles of Southern Chinese martial arts, both in China and abroad. The book proposes a theory of martial sound, which is the way we can hear music as martial arts and listen to hand combat as musicking, providing a way of discussing fighting rhythms in musical terms and a conceptual framework for analyzing how music can function as a form of self-defence. Participant-observation fieldwork for the book was undertaken over the course of eight years and spanned a time of significant transition. Both of the founding masters passed away, marking the end of an era and a time of reflection for the membership. The first female lion dancers also began performing during the fieldwork period, which reconfigured traditional constructions of gender. The book argues that while kung fu practitioners have traditionally used their interdisciplinary performances as a ritual to disperse negative energy for patrons, they extend that martial function in diaspora to become an empowering performance that challenges a history of race-based discrimination in Canada. Some audiences, however, now treat the ritual as an entertaining performance or a marker of identity, revealing multivalent meanings.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.063 | 0.009 |
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".