Losing Touch: An Embodiment Perspective on Coordination in Robotic Surgery
Bibliographic record
Abstract
Because new technologies allow new performances, mediations, representations, and information flows, they are often associated with changes in how coordination is achieved. Current coordination research emphasizes its situated and emergent nature, but seldom accounts for the role of embodied action. Building on a 25-month field study of the da Vinci robot, an endoscopic system for minimally invasive surgery, we bring to the fore the role of the body in how coordination was reconfigured in response to a change in technological mediation. Using the robot, surgeons experienced both an augmentation and a reduction of what they can do with their bodies in terms of haptic, visual, and auditory perception and manipulative dexterity. These bodily augmentations and reductions affected joint task performance and led to coordinative adaptations (e.g., spatial relocating, redistributing tasks, accommodating novel perceptual dependencies, and mounting novel responses) that, over time, resulted in reconfiguration of roles, including expanded occupational knowledge, emergence of new specializations, and shifts in status and boundaries. By emphasizing the importance of the body in coordination, this paper suggests that an embodiment perspective is important for explaining how and why coordination evolves following the introduction of a new technology.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.018 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".