Does Producing Scientific Articles Lead to Paralympic Podiums?
Bibliographic record
Abstract
The Olympic/Paralympic Games are world events that promote countries and their participants, and more particularly, those winning medals. The potential link between a country’s scientific productivity and its podium wins remains unknown for the Paralympic Games. This study aimed to (1) quantify the link between the production of Paralympic scientific articles and the medals won by countries during Summer/Winter Paralympic Games between 2012 and 2022, and (2) select the five most important articles published for all Paralympic sports. A bibliographic search of the Web of Science, PubMed, and Google Scholar databases was conducted. From the 1351 articles identified, 525 fulfilled the inclusion/exclusion criteria. The results showed a greater (7x) production of scientific articles relating to the Summer Paralympics compared to those relating to the Winter Paralympics. For the Summer Paralympics, there was a strong correlation (r = 0.79) between the number of medals and the number of scientific articles produced by a given country, while a low correlation (r = 0.12) was observed for the Winter Paralympics. Biomechanics-related articles represent almost 50% of the overall Paralympic publications. In conclusion, there is a strong link between scientific productivity and the number of medals won for the 2012–2022 Paralympic Games. Parasport Federations are strongly encouraged to promote the publication of more Paralympic research articles.
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 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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".