Bibliographic record
Abstract
The state of the environment can have significant impacts on sport. Sportsmen and women can be affected by environmental conditions such as air and water quality and exposure to harmful substances. Changes in climate and the loss of natural spaces may make participating in sport more difficult. The impact of the environment and especially of climate change becomes most obvious when looking at winter sports. If global warming affects the mountain snow cover, skiing or snowboarding and other winter sports will no longer be possible. There is growing consideration for the environment in the world of sports. The Olympic Movement, for instance, has incorporated the environment into its charter, alongside sport and culture. It has a Sport and Environment Commission to advise it on environment-related policy and has developed an Agenda 21 for sport and the environment to encourage its members to play an active part in sustainable development. Among the fruits of these initiatives was the first ever ‘green’ Olympic Games in Sydney in 2000, which showed clearly how development opportunities provided by the Games can be used to benefit the community and the environment. Since then UNEP has worked on both the Beijing and Vancouver Games and is planning with the most significant way sport can benefit the environment and sustainable development is through its popularity. Sports stars are among the world’s most famous and revered people. They display qualities we all need: courage, application, refusal to submit to adversity, leadership. Their potential as ambassadors, as promoters of sustainable ways of living, is enormous.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.060 | 0.012 |
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".