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Record W7047700063

The impact of recent policy revisions addressing doping and gender rules on women track and field student-athletes in China

2015· dissertation· en· W7047700063 on OpenAlexaff

Bibliographic record

VenueMspace (University of Manitoba) · 2015
Typedissertation
Languageen
FieldEngineering
TopicSuperconducting Materials and Applications
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsNucleofectionHyporeflexiaTSG101Gestational periodDurvalumabArticular cartilage damageFusible alloy
DOInot available

Abstract

fetched live from OpenAlex

Women’s involvement in sport has remained a critical issue in society for several decades. Sex verification and drug testing are two methods that have been used to regulate women’s eligibility to compete in international sports competitions based on their testosterone levels. Organizations such as the International Olympic Committee (IOC) and World Anti-Doping Agency (WADA) have published and updated policies and rules that set eligibility criteria for who can compete in women’s sport and under what conditions. However, the academic literature addressing Chinese women’s perspectives on international sex verification and drug testing policies available in English is extremely limited. This study investigates how recent policy revisions regarding doping and sex eligibility rules impact women student- athletes competing in track and field at the university level in China. Using qualitative research methods, this thesis analyzes the impact of recent doping and gender policies on a sample of Chinese female student-athletes.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.158
Threshold uncertainty score0.314

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0050.003
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.041
GPT teacher head0.297
Teacher spread0.256 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations0
Published2015
Admission routes1
Has abstractyes

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