Positive Frequency Dependence in Graffiti: An Empirical Case Study of Cultural Evolution
Bibliographic record
Abstract
Cultural traits can be seen to evolve by a process similar to natural selection. They are transmittable, variable, and the variants can have differential fitness. As a result, cultural evolution can in principle lead to non-random distribution of cultural traits. A limited number of studies have addressed the evolution of human cultural traits "in the wild," partly because culture is difficult to categorize into discrete units. Parallel to studying non-random species distributions in ecosystems due to natural selection, we have focused on investigating non-random distributions of cultural traits in a local environment. We used a collection of library study desks to categorize graffiti into content-based cultural traits, or "topics", and quantified the level of clustering for each topic as a measure of non-random distributions of topics on the desks. Clustering was found to occur for some topics but not others, and the level of clustering varied with topic in ways that are consistent with topic content characteristics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".