Physical Education and Sport in the Future World: The prevalence of physical inactivity—it is a pandemic
Bibliographic record
Abstract
Evolutionary studies have taught that, human body adapts to the environment and to changing life conditions. Among Neolithic, Bronze Age, and Iron Age women the dimension of the humerus bone was higher than present days female rowers and football players. With the development of the agriculture, over 6500 years, Medieval women lost humeral rigidity, due to the improved cultivation techniques, in comparison to Iron Age women 1. In contrast, mean tibial midshaft density, was lower among women in all prehistoric time periods than it was among living endurance runners 1. Mean tibial among football players also exceeded the particularly low values among Bronze Age and Medieval women, but not those of the Iron age. Climate influence on these changes in body shapes through changes in agricultural habits 2. Specifically, to maintain a constant surface area/body mass ratio, absolute body breadth should remain constant despite differences in body height. The development across epochs of lower limbs, reduced the energetic cost of human walking and running, albeit a linear relationship between stride length and lower limb length is not present 3, because stride length also depend from strength, and thus by muscular mass. Human beings perform remarkably well at endurance running (but not in sprint running in comparison to other species, because man is a predator and not a pray). Endurance running is a derived capability of the genus Homo, originating about 2 million years ago, and may have been instrumental in the evolution of the human body form 4. We know that, decreasing the prevalence of physical inactivity by 25% would avert 1.3 million deaths annually 5. Transhumanists theories see the potential in technologies for positively expanding and transcending human nature. In contrast, some philosophers are fearful of technology, suggesting that it will compound the deleterious effects of the colonization of the lifeworld, further constraining human autonomy 6. The consequences of reduced mobility during the coronavirus epidemic increased the pandemic inactivity, and should be counteracted by public health authorities with mass program of physical activity. \n1 Macintosh AA, Pinhasi R. Stock JT. Prehistoric women’s manual labor exceeded that of athletes through the first 5500 years of farming in Central Europe. Science Advances 3, 2017. \n2 Ruff BC. Climate and body shape in hominid evolution. J. Hum. Evol. 21, 81-105, 1991. \n3. Steudel-Numbers KL., Weaver TD., Wall-Scheffler CM. The evolution of human running: Effects of changes in lower-limb length on locomotor economy. J. Hum. Evol. 5,: 191-196, 2007. \n4. Bramble D., Lieberman D. Endurance running and the evolution of Homo. Nature 432, 345–352, 2004. \n5. Davis JC, Verhagen E, Bryan S, et al. Consensus Statement from the first Economics of Physical Inactivity Consensus (EPIC) Conference (Vancouver). British J. Sports Med. 48, 947-951, 2014. \n6. Edgar A. The hermeneutic challenge of genetic engineering: Habermas and the transhumanists. Med Health Care Philos. 12,157-67, 2009.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".