The developing understanding of Human Health and Fitness: 5. The Renaissance.
Bibliographic record
Abstract
Abstract Many factors contributed to a sudden upsurge of knowledge during the Renaissance, including a westward migration of the Greek scholars who had been expelled from Constantinople, the emergence of wealthy research sponsors, the printing of technical books and a relaxation of the previously tight ecclesiastical control over institutions of higher learning. Renewed opportunities for human dissection allowed a better understanding of body structures and thus a clearer insight into such physiological processes as the circulation of the blood, respiratory gas exchange and muscular contraction. Initially, the dominant centres of health science were in Bologna and Padua, but new medical schools quickly appeared in France, the Netherlands and Britain. Leading scholars at these institutions rejected the mediaeval traditions of asceticism and began to think in terms of developing the physical body as well as the soul. Moreover, several prominent physicians promoted regular exercise as an important factor in maintaining good health. Despite the introduction of licensing schemes for physicians and surgeons, many forms of treatment remained bizarre, and most were ineffective. Primary schools multiplied, and began to offer a semblance of education not only to families of the elite, but also to a growing proportion of children from the general population. But in most of these institutions, academic instruction was limited to long hours of rote learning. A few teachers such as da Feltra, Mulcaster and Montaigne were notable exceptions to this rule, seeing physical education as an important and integral component of the childs learning process. In England, many university authorities rejected student involvement in games such as football, seeing such activities as unwarranted distractions from academic study and religious observance. In European courts, a vigorous interest in sport was carried over from the Middle Ages, although some forms of activity became more stylized and required less physical effort. For the social elite, a further factor reducing habitual activity was the replacement of active commuting by the use of sedan chairs. A large segment of the general population continued to toil for long hours in the fields as agricultural labourers, but a growing proportion of citizens moved into the cities, where newly established Guilds offered sedentary employment. Most aspects of public health still attracted little attention from either the government or the public, and high death rates from epidemics such as bubonic plague remained the norm.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.018 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".