Dijital Çağda Kış Paralimpik Oyunları: Spor Teknolojilerinde İnovasyonun Etkileri
Bibliographic record
Abstract
The 21st century has been a period marked by accelerating innovation in sports technologies, during which the Paralympic movement has undergone a comprehensive digital transformation. This study systematically analyses the technologies implemented in the Winter Paralympic Games, held from Salt Lake City 2002 to Beijing 2022, in order to examine the direction and scope of this transformation. The review encompasses academic papers, institutional reports, manufacturer documentation, official organisational materials, and contemporary media content. Initially a rehabilitation-based field of sport, the Paralympic movement has evolved into an elite performance sport since the start of the century, largely influenced by technological advancements. The findings indicate that technological progress unfolded in three distinct phases: The initial phase was characterised by fundamental infrastructure adaptations, whereas the Vancouver 2010 and Sochi 2014 Games saw biomechanical measurement and sensory support systems become central to performance optimisation. In the 2018–2022 period, solutions based on 5G, artificial intelligence, and virtual reality facilitated holistic cyber-physical integration across organisational management and global broadcasting. This process is strengthening data-driven decision-making mechanisms, thus expanding inclusivity into the digital and cognitive dimensions.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".