Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it