The Potential Usefulness of Virtual Reality Systems for Athletes: A Short SWOT Analysis
Why this work is in the frame
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Bibliographic record
Abstract
Virtual reality (VR) systems (Neumann et al., 2017), which are currently receiving considerable attention from athletes, create a two- or three-dimensional environment in the form of emulated pictures and/or video-recordings where in addition to being mentally present, the athlete even often feels like he/she is there physically as well. As she/he interacts with and/or reacts to this environment, movement is captured by sensors, allowing the system to provide feedback.\n\nAs with every newly evolving technology related to human movement and behavior, it is important to be aware of the strengths, weaknesses, opportunities and threats (SWOT) associated with the use of this particular type of technology. SWOT analyses are widely utilized for strategic planning of developmental processes (Pickton and Wright, 1998; Tao and Shi, 2016) and it is of great interest to consider whether VR systems should be adopted by athletes or not. Aspects more inherent to the employed technologies of VR systems, and aspects more related to the application of VR systems with athletes are considered as strength/weaknesses and opportunities/threats, respectively. Analogously, SWOT analysis concerning another emerging technology involving sensors of individual parameters (i.e., “implantables”) has been performed (Sperlich et al., 2017).
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it