From Claude Bernard to the Batcave and beyond: using the life of the Caped Crusader to explore the integrative physiology of stress, exercise and injury
Bibliographic record
Abstract
The pop‐culture icon of Batman was used as an educational vehicle for popularizing physiology. Through his years of rigorous training the fictional human Batman pulled himself to near‐superhuman status. Evaluating the scientific possibility this means examining the Batman mythology from the perspective of integrative physiology. This is the main premise of the book “Becoming Batman: The Possibility of a Superhero” (Johns Hopkins University Press, 2008). The book was created in 4 stages involving: 1) a physiological assessment of occupations that parallel those of the fictitious Dark Knight; 2) articulating the concepts of homeostasis and stress; 3) connecting physiological adaptation in multiple and overlapping systems (e.g. nervous, musculoskeletal, endocrine); & 4) examining the adaptive capacity for these multiple systems to yield peak performance. Thus the approach was about explaining how the human body works and responds to exercise and training using Batman as the metaphor for ultimate performance. Main physiological concepts addressed in the text include the concept of homeostasis, Seyle's general adaptation syndrome, hierarchical organization of the nervous system, neural adaptations to skill training and motor learning, the neuropsychology of martial arts training and combat, pathophysiology of concussion, and the role for repeated trauma in the etiology of neurodegenerative diseases such as Alzheimer's. This approach was meant to bring scientific understanding to non‐specialists and the broader public by using an anchor point that was well understood (that of the physical image and impression everyone has of Batman) and then connecting science to that anchor. The objective is to share with other academics the process and positive and negative outcomes of using such an approach.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".