The Importance of Sport for Development (SfD) for the Social Recovery of the 2020 Pandemic. Directions for Policy Makers
Bibliographic record
Abstract
In 2010, Kofi Annan supported that sports power must be used as an agent for social change (Kofi Annan Foundation, 2010). Sports are a great dynamic not only for social change to be realized, but also for development within high performance sport systems, as they can be a conduit to peace. Furthermore, sports contribute to personal, but also to community development as it can teach people the importance of team and co-existing. During the 21st century, many important initiatives have been taken place aiming to boost the field called Sport for Development (SfD); nevertheless, different types of crisis such as financial (Földesi, 2014), and COVID-19 crisis (Wong et al., 2020) globally have complicated the development work that uses sport as a tool for various desired outcomes. The 2020 pandemic agitated the international community and made it difficult for sport activities to be operated. The quarantine periods and the various enforcement, laws, policies and recommendations have anecdotally caused more serious harms to groups of people (demographics like women, children, adolescents) (Bullinger et al., 2020). Individuals, especially women and kids were trapped during the quarantine with their abusers, having limited access to help and social activities; activities that aim to empower people, develop their skills and critical thinking. The current paper examines the SfD field and its importance for social development; briefly describes the effects of the lock down on the maximization of abuse, racism and discrimination and finally, proposes directions to be taken into consideration by policy makers so as to minimize the aforementioned phenomena and at the same time strengthen the SfD field.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".