MétaCan
Menu
Back to cohort
Record W3111366952 · doi:10.16926/par.2020.08.17

Biomechanical analysis of Yang’s spear turning-stab technique in Chinese martial arts

2020· article· en· W3111366952 on OpenAlexaff
Jianguo Kong, Xiuping Wang, Gongbing Shan

Bibliographic record

VenuePhysical Activity Review · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicMartial Arts: Techniques, Psychology, and Education
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsMartial artsSpearStabPsychologyVisual artsHistoryArtAnatomyMedicine

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.724
Threshold uncertainty score0.514

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.064
GPT teacher head0.425
Teacher spread0.360 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations18
Published2020
Admission routes1
Has abstractyes

Explore more

Same venuePhysical Activity ReviewSame topicMartial Arts: Techniques, Psychology, and EducationFrench-language works237,207