Coding culture: challenges and recommendations for comparative cultural databases
Bibliographic record
Abstract
Considerable progress in explaining cultural evolutionary dynamics has been made by applying rigorous models from the natural sciences to historical and ethnographic information collected and accessed using novel digital platforms. Initial results have clarified several long-standing debates in cultural evolutionary studies, such as population origins, the role of religion in the evolution of complex societies and the factors that shape global patterns of language diversity. However, future progress requires recognition of the unique challenges posed by cultural data. To address these challenges, standards for data collection, organisation and analysis must be improved and widely adopted. Here, we describe some major challenges to progress in the construction of large comparative databases of cultural history, including recognising the critical role of theory, selecting appropriate units of analysis, data gathering and sampling strategies, winning expert buy-in, achieving reliability and reproducibility in coding, and ensuring interoperability and sustainability of the resulting databases. We conclude by proposing a set of practical guidelines to meet these challenges.
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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 | 0.640 | 0.798 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.034 | 0.046 |
| Science and technology studies | 0.015 | 0.032 |
| Scholarly communication | 0.042 | 0.065 |
| Open science | 0.025 | 0.031 |
| Research integrity | 0.014 | 0.020 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".