Bibliographic record
Abstract
Introduction A major influence on Chomsky’s approach to the study of mind has come from rationalist philosophers such as Descartes. Like these thinkers, Chomsky’s work can be usefully seen in opposition to empiricist approaches to mind articulated by thinkers such as Locke. The aim of this chapter is to provide a conceptual backdrop against which one can locate some of Chomsky’s claims. I try to do this by outlining the different ways that the empiricist and rationalist traditions (actually, idealized versions of each) try to reconcile an apparent tension in epistemology (the theory of knowledge). The tension comes in trying to combine a theory of mind with a theory of truth to yield an account of how it is possible for humans to know anything, i.e. have true beliefs in some domain, especially true beliefs about the “outside world.” The two traditions endorse very different conceptions of the relation of minds to the world. However, it is possible to construct a shared conception of what the epistemological project amounts to that plausibly animates the details of the particular proposals that have been advanced. Doing this, I believe, allows for a better evaluation of the intuitions that drive these programs and thereby permits one to more fully appreciate some of Chomsky’s main philosophical proposals. In what follows, I will try to outline these general conceptions. I will then try to relate them to some of the concerns that Chomsky has raised in his linguistic and philosophical writing.
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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 | 0.034 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.046 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".