Grounding Responsibility: Helen Steward's Libertarianism and a Hemi-Incompatibilist Alternative
Bibliographic record
Abstract
This project has two purposes.The first is to evaluate Helen Steward's libertarian account, as presented in A Metaphysics for Freedom, and assess its ability to help ground moral responsibility.The second is to provide an alternative, Hemi-Incompatibilist account of a variety of responsibility that is compatible with the denial of agent causal control.An overview of a selection of accounts from the moral responsibility debate is provided, followed by a discussion of Helen Steward's account specifically.I then present my Hemi-Incompatibilist account, followed by the brief investigation of two similar views.I conclude that Helen Steward's libertarian account does not succeed in lending support to those hoping to ground a deserts-based variety of responsibility.I show that my Hemi-Incompatibilist account grounds another variety of responsibility, responsibility qua onus, that supports normative prescriptions and proscriptions despite the denial that agents possess regulative control.her constant support, calm, sense of humour, and tolerance in the face of my many administrative blunders.I have greatly benefited from interesting exchanges with my fellow postgraduate students on philosophical topics of all flavours.I would like to thank Jonathan Courtney, in particular, for his friendship, pleasant company, and the many heated and humourous debates that compelled me to better develop my arguments.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.027 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| 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".