The Sports in Training the Learners with Special Needs
Bibliographic record
Abstract
Sport has relatively recently ceased to be an applied institution of social life. If earlier the sport had a strictlydefined additional orientation (for example, it served the purposes of increasing the combat training of the army), thenafter the Second World War, sport becomes a means of demonstrating the prestige of the state, reaching theinternational level. Sport becomes a professional activity and an independent direction of the commercial life of thesociety.This paper is a preliminary research which gathers quantitative and qualitative information about social bonding for twocategories of stake-holders: intellectually disabled athletes and partners from mainstream schools, training andcompeting in unique teams. This approach aimed at: identifying the perception of self-concept and self-esteem among IDathletes in relation to their social integration, determining the way ID athletes think they are perceived by their schoolfriends and partners within Special Olympics events, identifying the positive effects on both personal and social levels,by participating in Sports events, identifying the partners’ perception related to the athletes’ abilities and possibility toadvocate in this respect.All data confirmed that regular training and Sports competition attendance leads to greater social inclusion, an interestingself-concept and self-esteem perception of the athletes, as well as certain inside ways of viewing the positive outcomesof delivering such educational programs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 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".