Bibliographic record
Abstract
Over the past decade, there has been a groundswell of support within the sport industry to be “good sports”, as evidenced by a growing number of, and commitment to, “giving” initiatives and “charitable” programs. Consider the following examples: • In 1998, the “Sports Philanthropy Project” was founded, devoted to “harnessing the power of professional sports to support the development of healthy communities.” (Sports Philanthropy Project, 2009) To date, this organization has supported and sustained over 400 philanthropic-related organizations associated with athlete charities, league initiatives, and team foundations in the United States and Canada. • In 2003, “Right To Play” (formerly Olympic Aid) the international humanitarian organization was established, which has used sport to bring about change in over 40 of the world's most disadvantaged communities. Of note is their vision to “engage leaders on all sides of sport, business and media, to ensure every child's right to play” (www.righttoplay.com). • In 2005, the Fédération Internationale de Football Association (FIFA) became one of the first sport organizations to create an internal corporate social responsibility unit, and soon thereafter committed a significant percentage of their revenues to related corporate social responsibility programs (FIFA, 2005).
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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.048 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".