On moral objectivity : can there be objective moral evaluation without invoking the existence of “queer” ontological properties?
Bibliographic record
Abstract
J.L Mackie, defender of the moral-error-theory, argues that to claim the existence of objective moral facts implies moral properties with inbuilt "to-be-pursuedness" - they would have to be intrinsically motivational. Since we do not know of the existence of any such properties, he argues that moral facts are "queer" things. I examine the positions of moral realists and anti-realists pointing out that it seems that one must either assert the existence of "queer" moral properties, or reject the truth functionality of categorical imperatives. After exploring the thought of Hilary Putnam and Emmanuel Levinas, I suggest an alternative explanation of the human moral experience that is free of "queer" moral properties. In this way, I believe to offer a more adequate explanation of human morality that defeats the false dilemma created by Mackie.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.058 |
| Scholarly communication | 0.011 | 0.021 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".