Bibliographic record
Abstract
In this riposte it will be argued that the critique by Peters (2013) of the theoretical foundation of evolutionary psychology misses the mark, and, in the process, unfortunately repeats many common and egregious misunderstandings. This reply will attempt to outline the real position of evolutionary psychologists with respect to modularity and the development, flexibility, and learning capacities of cognitive adaptations. In particular, evolutionary psychologists’ concept of the developmental target of naturally selected design will be made salient. I also aim to provide a more accurate treatment of the neurobiological and genomic issues at stake. In sum, it will be shown that Peters’ rendering of the theoretical foundation of evolutionary psychology is a straw man representation and that the real position of evolutionary psychologists is far more interesting once some of the nuances of their theoretical foundation are brought to light.
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.
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 | 0.014 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.029 |
| Scholarly communication | 0.006 | 0.019 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.021 | 0.035 |
| Insufficient payload (model declined to judge) | 0.005 | 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".