Fastest, Highest, Strongest: A Critique of High-Performance Sport
Bibliographic record
Abstract
Fastest, Highest, Strongest presents a comprehensive challenge to the dominant orthodoxy concerning the use of performance-enhancing drugs in sport. Examining the political and economic transformation of the Olympic Movement during the twentieth century, the authors argue that the realities of modern sport require a serious reassessment of current policies, in particular the ban on the use of certain substances and practices. The book includes detailed discussion of: * The historical importance of World War II and the Cold War in the development of a high-performance culture in sport * The changing Olympic project: from amateurism to a fully professionalized approach * The changing meaning of sport * The role of sport science, technology and drugs in pursuing ever-better performance * The major ethical and philosophical arguments used to support the ban on performance-enhancing substances in sport. Fastest, Highest, Strongest is a profound critical examination of modern sport. Its straightforward style will appeal to under- and post-graduate students as well as scholars of sports ethics and history, policy makers and all those interested in the changing nature of sport.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".