Bibliographic record
Abstract
Traces of Participation of Women in Physical Activities can be seen from the 18th century in different parts of the world in different ways but the participation of women in competitive sports especially the Olympics was showcased for the first time in the 1900 Paris Olympics. A total of six countries, the United States of America, the United Kingdom, Canada, Australia, Norway and India are selected for this paper. The age of the subjects are above 16 years and below 65 years. All the data presented in this paper are collected from secondary sources, such as Books, Research papers, Sports magazines, and Internet sources. After analysing the data it was found that America started to form professional sports leagues for women, and Canada Actively engaged a policy made for women in sports to replace the 1986 Sports Canada policy, In U.K some of the reasons for this lack of popularity for women in sports have come to be known are lack of media coverage and interest for men’s sports over women’s. The government of Australia encouraged women’s development in sports by forming women’s associations in the field of sports and also funded them. The Norwegian women also get the equal amount of media coverage compared with men. From the survey conducted by BBC over the citizens of India, we found that the greatest number of people can't name any female athlete. Although women have faced many barriers in sports, some of them are social, religious and economic. These barriers are the prime reason for women not participating in sports in the country.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".