MAPPING THE NETWORKED CONTEXT OF COPERNICUS, MICHELANGELO, AND DELLA MIRANDOLA IN WIKIPEDIA
Bibliographic record
Abstract
To discern the role social and cultural networks play in the emergence of preeminent historical figures and ideas in History, we use a method based on complex networks analysis to reveal emergent interactions in Wikipedia. We built a network constituted by derivative links, where nodes are connected if they are co-linked by other papers or co-link other papers within Wikipedia. We apply this method, focused on the structural distance, to three significant individuals associated with the Italian Renaissance: Copernicus, Michelangelo, and Pico della Mirandola. The results point to the effectiveness of this approach for discovering new knowledge about the interdisciplinary transactions between people and ideas coming from artistic, scientific and philosophical domains during this period. The emergent network reflects the apparently strong network-level interactions between Michelangelo and Mirandola’s clusters; the importance of Hermeticism across the three clusters; and how the so-called “knowledge dealers” related to Neoplatonism contribute to the depiction of the period by future historians. Finally, we advance the notion of “focus reading”, in which complex networks analysis allows us to build bridges between close and distant forms of reading historical evidence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".