Pluralistic folk psychology and varieties of self-knowledge: an exploration
Bibliographic record
Abstract
Turning the techniques we use to understand other people onto ourselves can provide an insight into the types of self-knowledge that may be possible for us. Adopting Pluralistic Folk Psychology, according to which we understand others not primarily by thinking about invisible beliefs and desires that cause behavior, but instead by modeling others as people - with rich characters, relationships, past histories, cultural embeddedness, personality traits, and so forth. A preliminary investigation shows that we understand ourselves at least in terms of our phenomenal states, informational states, perceptual states, traits, desires, and beliefs. I then appeal to empirical research to examine the accuracy of our sense of self-understanding in these ways, and argue that these are often non-veridical. Moreover, in our folk practices, we do not take our statements of self-understanding as infallible, but we allow others to help us see ourselves. While there is room for some improvement in our acurarcy, I conclude that our sense of self is largely a joint construct of self and others, and that looping effects play a significant role in what one’s self turns out to be. The self is a fluid thing that we are constantly creating through our actions and self-constituting thoughts, but it is a creation we do not make alone. Others help to create us, as we help to create them.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.054 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".