Bibliographic record
Abstract
Rugby is highly demanding from a physical standpoint. But anyone who has played or coached the sport knows that the mental side of the game separates the best players from the rest. Rugby Tough will give you the mental focus you need to give the game everything you've got. Learn how to apply mental skills effectively in specific match situations and get inside advice from those who've played, coached, and studied the game at every competitive level. Through Rugby Tough, you'll learn new ways to toughen your mindset and eliminate costly mental errors that inhibit your best performance. Rugby Tough starts with an emphasis on individual player development and the fundamental psychological skills you need to excel at the sport. In later chapters, the focus shifts to the importance of group dynamics and mental strategies in competitive play. From building team cohesiveness to defending and attacking mindsets, you'll discover all the tools you need to take your game to a whole new level. For the definitive word on mental preparation, Rugby Tough draws on the experience of coaches and sport psychologists from England, Ireland, New Zealand, Scotland, Canada, Australia, and the United States. To be among the world's best, you need the mindset of a champion. To prepare for the ultimate challenge, pick up the ultimate resource.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.327 | 0.143 |
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".