Bibliographic record
Abstract
Every great philosopher has important things to say about moral philosophy. Chomsky is no exception. Chomsky’s remarks on this topic, however, are not systematic. Instead, they consist mainly of brief and occasional asides. Although often provocative, they tend to come across as digressions from his central focus on linguistics and related disciplines, such as epistemology, philosophy of language, and philosophy of mind. Perhaps as a result, moral philosophers have paid relatively little attention to Chomsky over the past sixty years.This neglect is unfortunate. Chomsky’s insights into the nature and origin of human morality are fundamental and penetrating. They address deep philosophical problems that have shaped the aims of moral philosophy for centuries. They also reinforce many of the lessons Chomsky has taught about the nature and origins of human language. Elaborating upon these themes, this chapter begins by recounting two of Chomsky’s most extensive discussions of moral philosophy, each of which draws attention to the fact that, like linguistic knowledge, moral knowledge is an example of Plato’s problem: a complex mental competence characterized by a profound poverty of the stimulus. The chapter then places these remarks in a broader context by providing a brief discussion of mentalist, modular, and nativist theories of moral cognition from Plato to the present. Finally, the chapter responds to one prominent criticism of Chomsky’s naturalistic approach to moral philosophy, that of the late philosopher, Bernard Williams. I argue that Williams’ “Wittgensteinian” skepticism about moral rules is no more convincing than a similar skepticism about grammatical rules in the context of linguistic theory.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.022 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".