Darwinian epistemology : assessing the implications for reliable cognition in a non-adaptive domain of belief
Bibliographic record
Abstract
Charles Darwin and Alfred Wallace departed ways on the implications of evolution for human cognition. While Darwin argued that natural selection affected both the reliability and unreliability of human cognitive faculties, Wallace rejected the idea that natural selection could explain higher order intelligence. If Wallace is right, then Darwinian epistemology seems implausible. However, I argue that this position is false. In Chapter 1 I survey a history of Darwinian epistemology. In Chapter 2 I examine the Scope Objection to Darwinian epistemology: that evolution did not supply us with the natural cognitive capacities for achieving non-adaptive true beliefs. In Chapter 3 I respond to the Scope Objection by assessing Robert McCauley’s theory of natural cognition. In Chapter 4 I evaluate two difficulties with my response to the Scope Objection. I conclude that evolution is sufficient for explaining the reliability of human cognitive faculties in non-adaptive domains of belief.
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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 | 0.025 | 0.210 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".