Bibliographic record
Abstract
This article is an extended review of Michael Moore’s recent book Causation and Responsibility. In the first part of the article I summarize the book: I present it as an important contribution to our understanding of how the law now does treat, and how from a normative perspective it really should treat, the significance of causation for responsibility. In the second part of the article I present some criticisms. The criticisms focus on Moore’s meta-jurisprudential or methodological claims. Moore presents his position as “metaphysical”, and as using a “scientific” concept of causation. Moore claims that his methodology is superior to the so-called ordinary language methodology of HLA Hart and AM Honore in their Causation in the Law. I argue, first, that Moore’s repudiation of Hart and Honore is based on a misunderstanding of their approach, and that if we pay attention to his own approach as he executes it, rather than to his claims about his own approach, his approach is essentially no different from that of Hart and Honore. I argue, second, that Moore’s use of the terms “metaphysical” and “scientific” is confused when compared with what metaphysics as a genuine branch of philosophy is all about.
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".