Dealing with the Heterogeneity of Interpersonal Relationships in the Middle Ages. A Multi-Layer Network Approach
Bibliographic record
Abstract
Investigating the case of the Investiture Struggle in the diocese of Cambrai–Arras (c. 1100), this article aims at exploring some crucial issues for historians using social network analysis in the study of heterogeneous relationships. The study proceeds along three lines of enquiry. First, by establishing a hierarchy in the different types of relationships mentioned in the sources, it determines which of them are the most important to model and understand the structure of the network. Second, it demonstrates it is unnecessary to consider co-witnessing relationships (i.e. to be witnesses of a same charter) in the modelling of networks. Indeed, co-witnessing relationships do not help to improve our understanding of the structure of the parties at stake in a conflict. Finally, this paper deals with the importance of rank order in the witness lists. It demonstrates that, in the case of Cambrai, rank order does not have an influence on the global structure of the network. In other words, all individuals in the same witness list play a similar role in the network in terms of party structuring.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".