Bibliographic record
Abstract
W e often take propositional atti- tudes, such as "believes", "wants", "remembers", as characteristic of folk psychology, the way humans understand other humans.This can seem mysterious, since we express propositional attitudes as if they were relations between individuals and propositions, and such relations are not common in our descriptions of non-mental reality.(Churchland 1981, in giving reasons to be suspicious of folk psychology, lists this peculiar propositional ontology as a prime reason.See also Morton 2009.)In this paper I argue that this is wrong.Concepts of propositional attitudes are not the sui generis and essential element in folk psychology.Propositional attitude language is not the core of our everyday description of mind.Rather, this language can be seen as a way of describing something less exotic.So, on the picture I shall sketch, there are facts that are easily seen to be real relations between living organisms and their environments, which can be described in the same language as everything else.Then the language can be given just a little tweak, and we get a new
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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 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.010 | 0.022 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".