“Variation and selective retention” as an evolutionary epistemology: were Donald Campbell's life histories sufficient?
Bibliographic record
Abstract
Campbell's “evolutionary epistemology” is used more frequently to refer to extensions of Darwinism than other phrases, and his description of it as “variation and selective retention” is highly cited. However, we can still ask whether it is sufficient. The evidence from his classic essay is that he understood it to include somatic maintenance and reproductive growth, but omitted somatic growth and reproductive maintenance. We describe some of the complexity of the evolutionary ecology of life histories, including ecological and ecological versus social density-dependence and scale-dependence, and find that, interestingly, understood as a distinction between spending and investing, the traditional r versus K density-dependence distinction yields the same pattern of expected life history traits as does scale-dependence (although there should be other ways of distinguishing them). We then use this to fill in the missing somatic growth and offspring maintenance of Campbell's model of sociocultural evolution. In concluding, we emphasize the degree to which not only the evolutionary ecology of life histories but also the logic of population genetics and tree-building have been found relevant to the social sciences. Donald Campbell and David Hull, both now deceased, will be remembered as early modern pioneers of the theory of Darwinian sociocultural evolution.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.032 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| 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".