Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Theory of mind (ToM) is a great evolutionary achievement. It is a special intelligence that can assess not only one's own desires and beliefs, but also those of others. Whether it is uniquely human or not is controversial, but it is clear that humans are, at least, significantly better at ToM than any other animal. Economists and game theorists have developed sophisticated and powerful models of ToM and we provide a detailed summary of this here. This economic ToM entails a hierarchy of beliefs. I know my preferences, and I have beliefs (a probabilistic distribution) about your preferences, beliefs about your beliefs about my preferences, and so on. We then contrast this economic ToM with the theoretical approaches of neuroscience and with empirical data in general. Although this economic view provides a benchmark and makes useful suggestions about empirical tendencies, it does not always generate a close fit with the data. This provides an opportunity for a synergistic interdisciplinary production of a falsifiable theory of bounded rationality. In particular, a ToM that is founded on evolutionary biology might well be sufficiently structured to have predictive power, while remaining quite general. We sketch two papers that represent preliminary steps in this direction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it