Participation of the Pan-American Gymanastics Union in the 2011 World Gymnaestrada
Bibliographic record
Abstract
This paper focuses on the World Gymnaestrada (WG), the largest international Gymnastics for All festival, organized by the International Gymnastics Federation (FIG). The aim was to analyze the participation of the National Federation members of the Pan-American Gymnastics Union (PAGU) in the 14th edition of the WG, held in Lausanne, Switzerland, in 2011. The data collection was made by a semi-structured questionnaire with eight of heads of delegation of the eleven participating American national federation. The results highlight the largest delegations were Brazil, Canada and the USA, respectively, countries which also traditionally participate in this festival. The participants were majority female (74,5%) and the performance were strongly influenced by Artistic and Rhythmic gymnastics disciplines, as well as Dance. PAGU participation has increased in compare with past editions of the WG, ranking it as second in terms of continent with the largest number of participants (6,64%). Although the participation is strongly related to the economic factors, considering all the WG editions held in Europe. We conclude that non-competitive gymnastics still treat secondarily for most PAGU Member Federations, since they are focus on competitive gymnastics disciplines and especially on their national teams.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".