Safeguarding Our Children and the Vulnerable: Integration and a Platform for Prevention
Bibliographic record
Abstract
ABSTRACT The 2024 Olympic Games may be hailed as the greatest ever. But, though the Games business model may evolve, no new protections emerged for those at play. This paper explores the applicability of an integrative approach to publicly accessible data to increase the security of youth at play from all manner of abuse, including aggression, harassment and bullying. Although open source research (OSR) is a proven business intelligence strategy, it is not yet in our toolbox for safeguarding the vulnerable participants of amateur sports. In this paper, the author demonstrates the potential for an integrative prevention platform, with foundational databases, AI analytics and professional analysis to assure accountability, transparency and adherence to privacy regulations. An integrative platform can serve in better screening of hiring candidates, verifying identity and confirming credentials, monitoring sport management behaviours, educating about risks and threats and managing partnerships, all while supporting preventive action, measuring performance and reporting progress.
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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 | 0.019 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".