Olympic Champion Sara Simeoni Talks about Gender Barriers in Sport and Medicine: Culture, Opportunities and Resources
Bibliographic record
Abstract
An overview of the relationship between elite athletes’ sport activity and medicine was presented during a conference with guest speaker Mrs. Sara Simeoni, Italian Olympic Games high jump champion (one gold medal, in Moscow, and silver medals in Montreal and Los Angeles). She has had a particularly prolonged career for this type of sport activity, and her endorsement and support, since she is a recognized icon of sport excellence, is very important. This is particularly true in relevant and sensitive topics such as gender inequalities, physical exercise and sport ethics. During the conference, the relationship between nutrition, physical activity and health, in both medicine and social life, were also covered. In reality, we still find that the cultural, economic, societal and political barriers are present and, currently, in some regards, they are even stronger than in the past. The interplay between evidence-based medicine, elite athletes’ training, physical fitness practice and physical exercise intervention in health and disease is still being developed. Defining methods and clarifying multidimensional outcomes should, as much as possible, be pursued, and sustainable strategies and tools should be discussed.
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 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".