Anatomy as embodied resistance in an age of digital abstraction
Bibliographic record
Abstract
Amid the accelerating integration of digital technologies in the health professional education, anatomy education with an emphasis on engagement with real human bodies can provide a crucial counterweight to digital abstraction. Rapid advances in artificial intelligence and algorithm-driven medicine may lead to the intrinsic value of embodied human experience being overlooked. Hands-on anatomy education-through practices such as dissection and direct engagement with human remains-can reaffirm the reality of the human body and nurture the empathy and ethical reflection essential for an empathy-grounded practice in the health professions. By engaging directly with the body, learners experience a tactile encounter that transcends what digital simulations and abstract data can offer. Grounded in phenomenology and the concept of the "lived body," this approach challenges the notion that our physical existence can be entirely captured by computational models. Instead, it emphasizes that the sensory, emotional, and ethical dimensions of human existence are crucial for both medical understanding and compassionate practice. Throughout history, anatomical inquiry has long served as a site for confronting mortality, identity, spiritual inquiry, and social inequities. The concept of the anatomical gaze onto the human body illustrates how historical practices of dissection and anatomical illustration reveal the power dynamics and ethical challenges inherent in observing and interpreting the human body. In an age where online interactions increasingly shape human connection, the tactile lessons of anatomy provide a vital safeguard against the erosion of empathy and the dehumanization of patient care. Thus, anatomy education is both an ethical and political imperative today: By grounding future healthcare professionals in the tangible realities of human existence, anatomy education will need a balanced approach-one that embraces technological advancements while honoring the complexity and dignity of the human body.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.057 |
| Scholarly communication | 0.013 | 0.019 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".