Running for their lives : the extraordinary story of Britain's greatest distance runners
Bibliographic record
Abstract
In 1928 two extraordinary Englishmen competed in an unprecedented and fearsome event - a transcontinental road race across America that required them to run an average of 40 miles for 80 consecutive days. They were to become the most famous long-distance runners in the world: yet history has forgotten them. Peter Gavuzzi was a young working-class ship's steward, while Arthur Newton was a middle-aged intellectual who had taken up running to make a political point. Though separated by class, education and age, they became close friends and formed a successful business partnership as endurance athletes. They raced in 500-mile relays, in 24-hour events, in snowshoes and against horses; and they became the stars of a craze for endurance events that swept across depression-era North America. But as professional runners they were eschewed by the amateur running elite. Set against a turbulent backdrop of 1920s South Africa, 1930s Canada, war-torn France and 1950s Britain, Running for Their Lives is a story peopled with remarkable characters, unimaginable feats and tragic twists of fate. More importantly it is a homage to two inspirational and eccentric men who only now receive the recognition they so richly deserve.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".