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Record W2004429261 · doi:10.1080/14763141.2011.569567

Gender differences in foot shape: a study of Chinese young adults

2011· article· en· W2004429261 on OpenAlex
Youlian Hong, Lin Wang, Dong Xu, Jing Xian Li

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSports Biomechanics · 2011
Typearticle
Languageen
FieldEngineering
TopicLower Extremity Biomechanics and Pathologies
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsFoot (prosody)Girth (graph theory)Foot deformityMathematicsOrthodonticsDeformityMedicineSurgery

Abstract

fetched live from OpenAlex

One important extrinsic factor that causes foot deformity and pain in women is footwear. Women's sports shoes are designed as smaller versions of men's shoes. Based on this, the current study aims to identify foot shape in 1,236 Chinese young adult men and 1,085 Chinese young adult women. Three-dimensional foot shape data were collected through video filming. Nineteen foot shape variables were measured, including girth (4 variables), length (4 variables), width (3 variables), height (7 variables), and angle (1 variable). A comparison of foot measures within the range of the common foot length (FL) categories indicates that women showed significantly smaller values of foot measures in width, height, and girth than men. Three foot types were classified, and distributions of different foot shapes within the same FL were found between women and men. Foot width, medial ball length, ball angle, and instep height showed significant differences among foot types in the same FL for both genders. There were differences in the foot shape between Chinese young women and men, which should be considered in the design of Chinese young adults' sports shoes.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.124
Threshold uncertainty score0.840

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.031
GPT teacher head0.219
Teacher spread0.188 · 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