Bibliographic record
Abstract
Naturalizing pragmatism A naturalizing move The question for this chapter is what kind of rational warrant the mind/brain might have for believing mysteries, for that which it can never fully understand. In the tradition of Chomsky's linguistics and in conformity to how relevance theory conceives of itself, both linguistics and pragmatics are parts of cognitive psychology. However, in our accounts of semantic content, we are dependent on philosophy, on the manifest image. Psychological theories of representation are not extricable from philosophical analyses of key concepts. Conversely, both philosophical psychology and epistemology, to the degree they construct theories, should be consistent with the results of science, however distinct the method of inquiry. In fact, inextricability is obvious from the histories of both behaviourism and cognitive science. To naturalize an area of inquiry is to investigate it using the methods of natural science. Chomsky proposes that naturalization be without any metaphysical connotations (Chomsky, 2000: 76). Citing Baldwin (1993), he notes that this differs from Dennett's ‘metaphysical naturalism’ in which certain metaphysical assumptions, for example, forms of Platonism, are excluded as not consistent with his view of natural science. To the degree that naturalization is successful, the domain is transformed from that of common sense as elaborated and clarified by philosophical inquiry and becomes part of Sellar's scientific image of humanity. In psychology, naturalization has consisted of transforming folk psychology and its terminology, through philosophical psychology, to psychology as a special science. Such progression is normal in the history of science.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.024 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".