Bibliographic record
Abstract
I argue that having a theory of mind requires having at least implicit knowledge of the norms of the community, and that an implicit understanding of the normative is what drives the development of a theory of mind. This conclusion is defended by two arguments. First I argue that a theory of mind likely did not develop in order to predict behavior, because before individuals can use propositional attitudes to predict behavior, they have to be able to use them in explanations of behavior. Rather, I suggest that the need to explain behavior in terms of reasons is the primary function of a theory of mind. I further argue that in order to be motivated to offer explanations of behavior, one must have at least an implicit understanding of appropriate behavior, which implies at least an implicit understanding of norms. The second argument looks at three cases of nonhuman animal societies that appear to operate within a system of norms. While there is no evidence that any species other than humans have a theory of mind, there is evidence that other species have sensitivity to the normative. Finally, I propose an explanation for the priority of norms over a theory of mind: given an understanding of norms in a society, and the ability to recognize and sanction violations, there developed a need to understand actions that violated the norms, and such explanations could only be given in terms of a person's reasons. There is a significant benefit to being able to explain behavior that violates norms, because explanations of the right sort can also serve to justify behavior.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.018 |
| Scholarly communication | 0.005 | 0.014 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".