Understanding human enhancement technologies through critical phenomenology
Bibliographic record
Abstract
Human enhancement technologies raise serious ethical questions about health practices no longer content simply to treat disease, but which now also propose to "optimize" human beings' physical, cognitive and psychological abilities. These technologies call for a reassessment of our relationship to health, the human body and the body's organic, identity and social functions. In nursing, such considerations are in their infancy. In this paper, we argue for the relevance of critical phenomenology as a way to better understand the ethical issues related to human enhancement technologies (HET). In so doing, we seek to problematize HET and assess their influence on the future development of nursing science and practice. It is difficult to anticipate the concrete effects of HET, we suggest, because these practices reconfigure the meaning of normativity and disorient our conventional ethical landscape. In this context, we argue that the later work of Martin Heidegger and Michel Foucault invites a critical perspective into how techno-scientific discourses modify our relationship to care, to health and to our own social and corporeal identities. Despite the traditional philosophical opposition between phenomenology and critical theory, we maintain that a hybrid critical phenomenological approach opens new ways to assess the integration of technology and practice. Our analysis understands HET as a process of "hybridization" between technological objects and human subjects. Critical phenomenology thus effectively questions anthropocentric definitions of technology, challenges the dichotomy between curative treatment and enhancement and, finally, prompts valuable reflection on the implications of HET for nursing theory and practice.
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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 | 0.034 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.065 |
| Scholarly communication | 0.014 | 0.022 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".