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Record W7033921689

Rio, Tokyo Paralympic Games and beyond: How to Prepare Athletes with Motor Disabilities for Peaking

2017· book· en· W7033921689 on OpenAlexaboutno aff

Bibliographic record

VenueDirectory of Open access Books (OAPEN Foundation) · 2017
Typebook
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsnot available
Fundersnot available
KeywordsAthletesExcellenceCeremonyOpening ceremonyTrack and field athleticsEvent (particle physics)Quarter (Canadian coin)Closing (real estate)
DOInot available

Abstract

fetched live from OpenAlex

In 1960, the 9th Annual International Stoke Mandeville Games were supported, for the first time, by the Italian Olympic Committee. Taking place six days after the Closing Ceremony of the XVII Olympic Games, the paralympic games for disabled athletes were born. From Roma in 1960 to London in 2012, the Paralympic Games grew in terms of athletes’ number from 400 to 4,237, and now brings together more than 164 nations (Perret, 2015). The word “Paralympic” derives from the Greek preposition “para” (beside or alongside) and the word “Olympic”. Paralympics want to be the parallel Games to the Olympics and illustrate how the two movements exist side-by-side (Paralympics – History of the Movement, 2016). Now taking place after the Olympics Games, the Paralympic Games are the pinnacle of the career of athletes with physical impairments and have become the second largest sport event in the world (Perret, 2015; Paralympics – History of the Movement, 2016; Gold and Gold, 2011). The first statement of the vision of the International Paralympic Committee (IPC), i.e. “to create the conditions for athlete empowerment through self-determination” (Paralympics – History of the Movement, 2016; International Paralympic Committee, 2016), shows the importance of the place of the athlete with an impairment at the heart of the Paralympic Movement. The ultimate aim of the IPC is « to enable Paralympic athletes to achieve sporting excellence and inspire and excite the world. » (International Paralympic Committee, 2016). The performance level of athletes with an impairment improved to a point that, in the present days, sport news and world sport movements focus on the potential advantage of artificial limbs among athletes with amputations and their integration in able-bodied competitions (Burkett, 2010). However, they do not represent the totality of athletes with an impairment at the Paralympic Games. Athletes with other physical impairments (visual deficit, spinal cord injury, cerebral palsy or else) are eligible to compete. These impairments induce typical functional and physiological (e.g., cardiovascular, thermoregulatory) responses to exercise. For example, spinal cord injury (athletes with tetraplegia or paraplegia) causes thermoregulatory impairment (Goosey-Tolfrey et al., 2008) and individuals with cerebral palsy have also demonstrated higher thermal and metabolic strain than matched controls during treadmill walking in the heat (Maltais et al., 2004). Thus, hyperthermia among these athletes with an impairment alters their performance compared to their Olympic counterparts (Bhambhani, 2002). Mechanical performance analysis, the description of physiological responses according to the functional impairment or else the response to training and the relationship between laboratory and field testing responses are different parts of a package introduced here to address the aim of the IPC: to enable Paralympic athletes to achieve sporting excellence (Paralympics – History of the Movement, 2016; International Paralympic Committee, 2016).

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.024
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0050.003
Scholarly communication0.0040.004
Open science0.0010.006
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0190.006

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 head0.128
GPT teacher head0.453
Teacher spread0.325 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2017
Admission routes1
Has abstractyes

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