Philosophical reflections on physical strength : does a strong mind need a strong body?
Bibliographic record
Abstract
Preface by M. Andrew Holowchak, Muhlenberg College Acknowledgments Foreword by J.S. Russell, Langara College Part I: Strength' Does Powerlifting Really Test for Power? - Samer Saab, Lebanese American University What It Really Takes to be the World's Strongest Man - M. Andrew Holowchak, Muhlenberg College Reflections, and Otherwise, on 'Strength - Zydrunas Savickas, Strongman Legend Part II: & Being Heidegger and Schwarzenegger: Being and Training - Jerry Sandau The Ki to in the Martial Arts - Allan Back, Kutzlown University Part III: Strength, Beauty, & Knowing Philosophical and Practical Considerations for a Strongman Contest - Terry Todd, University of Texas/Powerlifting and Weightlifting Champion Aesthetic and Epistemic Aspects of 'Iron Games - John Bender, Ohio University Part IV: & Ethics Charles Atlas and the Meaning of Life - Ray Belliotti, State University of New York, Fredonia Hercules' Dilemma: Really a Virtue? - Heather Reid, Morningside College Part V: & Gender Extreme Beauty: Size and Sexism in Women's - Jill Mills, Two-Time ESPN's World's Strongest Woman Is Women's Bodybuilding 'Unfeminine'? - Melina C. Bell, Washington and Lee University Part VI: & Technology Gene Doping and Strength - Angela Schneider, University of Western Ontario Strong Medicine: Drugs and Sports Redux - Michal Lavin.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.016 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".