Bibliographic record
Abstract
John is currently thinking that the sun is bright. Consider his occurrent belief or judgement that the sun is bright. Its content is that the sun is bright. This is a truthevaluable content (which shall be our main concern) because it is capable of being true or false.1 In virtue of what natural, scientically accessible facts does John’s judgement have this content? To give the correct answer to that question, and to explain why John’s judgement and other contentful mental states have the contents they do in virtue of such facts, would be to naturalize mental content. A related project is to specify, in a naturalistically acceptable manner, exactly what contents are. Truth-evaluable contents are typically identied with abstract objects called “propositions,” e.g. the proposition that the sun is bright. According to one standard story, this proposition is constituted by further abstract objects called “concepts”: a concept that denotes the sun and a concept that denotes brightness. These concepts are “combined” to form the proposition that the sun is bright. This proposition is the content of John’s belief, of John’s hope when he hopes that the sun is bright, of the sentence “The sun is bright,” of the sentence, “Le soleil est brillant,” and possibly one of the contents of John’s perception that the sun is bright, or of a painting that depicts the sun’s brightness.2 This illustrates the primary theoretical role of propositions (and concepts). Saying of various mental states and/or representations that they express a particular proposition P is to pick out a very important feature that they have in common. But what exactly is this feature? What are propositions and concepts, naturalistically speaking? Having raised this important issue, I will now push it into the background, and focus on the question of how mental statescan have contents, rather than on what contents are, metaphysically speaking. (That said, the most thoroughly naturalistic theories of content will include an account of propositions and concepts – compare the thoroughly naturalistic Millikan [1984], for instance, with McGinn [1989].) Whatever the ultimate nature of contents, the standard view among naturalists is that content is at least partly constituted by truth conditions (following e.g. Davidson [1967] and Lewis [1970] on the constitution of linguistic meaning). This review, then, will focus on naturalistic accounts of how mental states’ truth conditions are determined. That said, “content” is clearly a philosophical term of art, so there is a large degree of exibility as to what aspects of a mental state count as its content, and therefore what a theory of content ought to explain. For example, is it possible for me, you, a blind person, a robot, a chimpanzee, and a dog to share the belief “that the stop sign is red,” concerning a particular stop sign? Clearly, there are differences among the mental states that might be candidates for being such a belief, but it is not immediately obvious which of those differences, if any, are differences in content. It seems that contents pertain both to certain mental states (like John’s judgement) and to representations (like a sentence).3 It would simplify matters a lot if contentful mental states turned out to be representations also. This is a plausible hypothesis (see Chapters 7, 10, 17, and 23 of this volume), and almost universally adopted by naturalistic theories of content. On this hypothesis, the content of a particular propositional attitude is inherited from the content of the truth-evaluable mental representation that features in it. What we are in search of, then, is a naturalistic theory of content (including, at least, truth conditions) for these mental representations, or in Fodor’s (1987) terms, a “psychosemantic theory,” analogous to a semantic theory for a language. Soon we will embark on a survey of such theories, but rst, a couple of relatively uncontroversial attributive (ATT) desiderata.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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 teacher head, 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".