Bibliographic record
Abstract
Introduction. Part I: Rugby: Roots, Boots, and All. Chapter 1. Rugby's Beginnings, Allure, and a Basic Overview. Chapter 2. The Basics. Chapter 3. Grab Your Rugby Gear. Part II: Getting Down and Dirty. Chapter 4. Location, Location, Location: Positions on the Pitch. Chapter 5. Laying Down the Laws. Chapter 6. Understanding the Fundamentals. Chapter 7. Playing the Game. Chapter 8. The Art of Scrimmaging. Chapter 9. Line-Outs: Restarting from Touch. Chapter 10. Individual Skills. Chapter 11. Tactics and Teamwork. Chapter 12. Talented Training. Part III: Welcome to the Oval Planet. Chapter 13. The World Cup. Chapter 14. The International Calendar. Chapter 15. North American Rugby. Chapter 16. Amateur Rugby in North American. Chapter 17. Collegiate, High School, and Youth Rugby in North America. Part IV: Coaching and Refereeing. Chapter 18. Coaching. Chapter 19. Coaching Certification and Advancement. Chapter 20. Managing the Game - The Referee. Part V: Following the Game: The Informed Fan. Chapter 21. Get Your Game - Rugby on TV. Chapter 22. Spectating and Staying in Touch with Rugby News. PART VI: The Part of Tens. Chapter 23. The Ten Greatest North American Players. Chapter 24. The Ten Best Rugby Moments. Chapter 25. Ten Peculiar Facts about Rugby. Appendix A. U.S. and Canada Tests. Appendix B. Glossary. Index.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.648 | 0.445 |
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".