Bibliographic record
Abstract
The purpose of this paper is to discuss critically selected aspects of the current discussion on barefoot running, specifically differences between barefoot and shod running in kinematics and kinetics, training effects, performance and economy and injury frequency. The kinematics and kinetics depend on many different factors, including surface, shoe, running speed and subject. In general, hard surfaces are associated with a flatter foot landing. However, the inter-individual differences are substantial and it is not appropriate to associate barefoot running with toe landing and shod running with heel landing. The training effects for the small muscles crossing the ankle joint are small during running and substantially higher for movements such as side shuffling, independent of footwear. The additional mass added to the foot by the shoe seems not to have a negative effect on performance until at a ‘threshold mass’ of about 200 to 250 g. The additional work due to the damping of vibrations of soft tissue compartments seems not to depend primarily on the footwear but rather on the individual comfort of the runner. To the knowledge of the authors, there is no conclusive evidence that barefoot running has more, equal or less injuries than shod running. From a biomechanical point of view, injuries are a result of overloading of a given structure. The internal active forces in the lower extremities are about 500% higher than the internal impact forces. Consequently, these impact forces may not be the major reason for potential running injuries.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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".