How Discourse on Moral Bioenhancement and Bioethics Has Evolved Since Their Inception
Bibliographic record
Abstract
This paper aims to analyze the discourse around transhumanist ideologies. Transhumanism encompasses a wide range of perspectives and scientifically backed theories regarding the future of humanity and the self-directed technological evolution of the species. Transhumanism is an important lens through which to view the evolutionary potential of our species as many transhumanist intellectuals believe that collaboration with modern and future technology can solve many long-running problems in this world. Since transhumanism encapsulates a vast range of topics and ideas, this project will focus particularly on the limitations of the physical and cognitive human condition and how medical advancement has impacted and will continue to impact the capacities of humanity using technology. Thus, the controversial transhumanist topics that this paper will aim to analyze are bioethics and moral bioenhancements considering the societal effects of biopower and biopolitics. Will humanity be receptive toward technological advancements as our self-directed evolution grows more radical and further from the traditional biological nature of humanity? This question will be answered by analyzing how receptive and accepting people have been to these transhumanist ideologies in the past. Throughout history, some have been resistant to change, to evolution, and to technology while others have been accepting and encouraging. This controversy has been observed with certain historical and modern medical and biological advancements such as vaccines, pharmacology, genetic engineering, cosmetic surgery, and elective physical and cognitive enhancement by means of 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 | 0.039 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.016 | 0.092 |
| Scholarly communication | 0.026 | 0.030 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.016 | 0.023 |
| 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".