Wikipedia as a cultural lens: a quantitative approach for exploring cultural networks
Bibliographic record
Abstract
The structure of cultural networks emerges from complex interactions among many elements related to people, ideas, and objects. However, these interactions can be very subtle and difficult to quantify, precluding a quantitative analysis of the cultural networks that can be crucial to understanding complex dynamics better. In this work, we propose a new approach that combines the formalism of complex networks, the structural relationship between nodes, and the corpus of Wikipedia to map and analyse the interactions among cultural entities. To test the proposed methodology, we study the case of the interdisciplinary cultural network connecting art, science, and philosophy in Europe in the seventeenth century. The results are aligned with well-established historical knowledge of the period and, more importantly, provide new insights to unveil how elements in these networks interact with each other. In particular, we found that nodes within a given cluster, related respectively to art, science or philosophy, interact with nodes in the same cluster following a core-periphery behaviour. In contrast, inter-cluster interactions across disciplines follow a power law distribution.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".