From applied ethics to empirical ethics to contextual ethics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Bioethics became applied ethics when it was assimilated to moral philosophy. Because deduction is the rationality of moral philosophy, subsuming facts under moral principles to deduce conclusions about what ought to be done became the prescribed reasoning of bioethics, and bioethics became a theory comprised of moral principles. Bioethicists now realize that applied ethics is too abstract and spare to apprehend the specificity, particularity, complexity and contingency of real moral issues. Empirical ethics and contextual ethics are needed to incorporate these features into morality, not just bioethics. The relevant facts and features of problems have to be identified, investigated and framed coherently, and potential resolutions have to be constructed and assessed. Moreover, these tasks are pursued and melded within manifold contexts, for example, families, work and health care systems, as well as societal, economic, legal and political backgrounds and encompassing worldviews. This naturalist orientation and both empirical ethics and contextual ethics require judgment, but how can judgment be rational? Rationality, fortunately, is more expansive than deductive reasoning. Judgment is rational when it emanates from a rational process of deliberation, and a process of deliberation is rational when it uses the resources of non-formal reason: observation, creative construction, formal and informal reasoning methods and systematic critical assessment. Empirical ethics and contextual ethics recognize that finite, fallible human beings live in complex, dynamic, contingent worlds, and they foster creative, critical deliberation and employ non-formal reason to make rational moral judgments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.075 | 0.551 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.017 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.015 | 0.137 |
| Insufficient payload (model declined to judge) | 0.001 | 0.013 |
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 it