Are the New Mass Media Subverting Cultural Transmission?
Bibliographic record
Abstract
Popular culture is a subcategory of culture. Today, mass and new media appear to be interfering with the evolved mechanisms that permit the acquisition and editing of culture. We know surprisingly little about these cognitive attentional processes that enable the information acquisition and editing packed into the term “cultural transmission.” It was Michael Chance who first concluded that we attend to and learn preferentially from those high in status. For Chance, high status based on fear leads to agonistic attention and a constricted type of learning, while hedonic attention based on respect permits much broader learning possibilities. If Chance's theories are supported, then it would follow that much of the current unpredictability of popular culture and culture change in general reflects the replacement of family and community high-status figures by influential media celebrities, thereby damaging the transmission of local culture. Chance's approach would also explain why we seem to find it difficult to pay attention to those low in status and power. There may be attractors of attention involved in cultural transmission in addition to status, including physical attractiveness. We consider, from an evolutionary perspective, various researchable hypotheses that stem from Chance's and related work and from ethnography, we discuss this work's implications for how we understand culture and “popular culture,” and we argue that the kind of research in cognitive and evolutionary psychology we espouse is also needed for the next generation of mathematical models of gene–culture coevolution. We conclude with a list of research questions.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".