Bibliographic record
Abstract
An evolutionary lens can inform the study of cultural forms in a myriad of ways. These can be construed as adaptations, as exaptations (evolutionary byproducts), as gene–culture interactions, as memes, or as fossils of the human mind. Products of popular culture (e.g., song lyrics, movie themes, romance novels) are to evolutionary cultural theorists what fossils and skeletal remains represent to paleontologists. Although human minds do not fossilize or skeletonize (the cranium does), the cultural products created by human minds do. By identifying universally recurring themes for a given cultural form (song lyrics and collective wisdoms in the current article), spanning a wide range of cultures and time periods, one is able to test key tenets of evolutionary psychology. In addition to using evolutionary psychology to understand the contents of popular culture, the discipline can itself be studied as a contributor to popular culture. Beginning with the sociobiology debates in the 1970s, evolutionary informed analyses of human behavior have engendered great fascination and animus among the public at large. Following a brief summary of studies that have explored the diffusion of the evolutionary behavioral sciences within specific communities (e.g., the British media), I offer a case analysis of the penetration of evolutionary psychology within the blogosphere, specifically the blog community hosted by Psychology Today.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".