On the Verifiability of Evolutionary Psychological Theories: An Analysis of the Psychology of Scientific Persuasion
Why this work is in the frame
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Bibliographic record
Abstract
Evolutionary psychological theories have engendered much skepticism in the modern scientific climate. Why? We argue that, although sometimes couched in the language of unfalsifiability, the skepticism results primarily from the perception that evolutionary theories are less verifiable than traditional psychological theories. It is more difficult to be convinced of the veracity of an evolutionary psychological theory because an additional layer of inference must be logically traversed: One not only has to be persuaded that a particular model of contemporary psychological processes uniquely predicts observed phenomena, one must also be persuaded that a model of deeply historical processes uniquely predicts the model of psychological processes. This analysis of the psychology of scientific persuasion yields a number of specific suggestions for the development, testing, and discussion of evolutionary psychological theories.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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