Biomechanical analysis of Yang’s spear turning-stab technique in Chinese martial arts
Bibliographic record
Abstract
The Yang’s spear turning-stab was a legendary technique, applied in ancient battles in China. It resulted in numerous famous winnings. The mythical aspect of the technique is a victory in fleeing and back-facing a fighter. Now the skill is a spear technique of Chinese martial arts that is learned and excised by many Chinese Gungfu practitioners. Due to a dearth of scientific study on the skill, the uniqueness and its winning secretes are still unknown. The aim of this study is to demystify the skill by using a synchronized measurement of 3D motion capture (VICON 12 camera system), stab-force measurement (AMTI force platform). Six Gungfu athletes with more than 30 years training experience participated in the study. Both the Yang’s spear turning-stab (used by a fleer) and spear forward-stab (used by a chaser) were measured and biomechanically analyzed. The results reveal that there would be six secrets for its historical successes. They are 1) showing weakness (i.e. pretend to be defeated), 2) shortening the stab for quick turning, 3) hiding the stab for a covert attack, 4) leaving less reaction time for opponent, 5) generating higher stab-force than opponent, and 6) leaning backward for a stable stab-posture. These secrets identify elements necessary for systematic training toward a reliable execution of the skill. This skill shows the delicate characteristics of Chinese martial culture. Learning and training the skill would benefit trainees both physically and culturally.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".