Bibliographic record
Abstract
Health care is developing rapidly. So are its correlates, such as health care technology, research, education, administration, communication, and more. Such change requires ethical deliberation, as change that is not ethically guided poses unnecessary risks. This may be particularly true in relation to health care, which impacts some of the most central domains of human life. Bioethics addresses issues of health care ethics. It consists of approaches that attempt to resolve moral conflicts, viewed as conflicts among moral values that may each be acceptable in some circumstances but that require prioritizing when combined with other moral values in particular circumstances. Such approaches include the application of theories such as consequentialism, which refers to outcomes (such as happiness); deontology, which refers to duties or intentions (such as the obligation not to lie); virtue ethics, which refers to character features (such as honesty); principlism, which refers to the four principles of upholding autonomy (self-determination), beneficence (best interests), non-maleficence (least harm), and justice (as fairness, for example); and more (Beauchamp & Childress, 2009; Rudnick, 2001; Rudnick, 2002). Bioethics ranges across many areas and its scope is still broadening. Some of its emerging areas address organizational bioethics, global bioethics, and much more. This book focuses on a sample of emerging as well as more established areas of bioethics. The chapters were selected according to various considerations, such as interest of authors. Yet in spite of not being exhaustive, this book illustrates the range and impact of bioethics in the 21st century. As part of that, some of the chapters go beyond fact and theory into some speculation (the chapters with more speculative topics can be found near the end of this book). We think this is necessary for bioethics to be constructive, recognizing that speculation must be checked by common sense as well as by known fact and theory. Indeed this is how much of bioethics proceeds (Rudnick 2007). There are areas of bioethics that are not covered in this book, such as neuroethics, enhancement ethics, ethics of genetics, and more. We cannot touch on most of them here. Still, we would like to highlight neuroethics as a likely paradigm of an emerging area in bioethics. Neuroethics can be defined in part as the ethics of neuroscience (http://en.wikipedia.org/wiki/Neuroethics). More specifically, it can be viewed in part as
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.037 | 0.014 |
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".