Paper year
2026
Paper dossier
Inferring Communities of Medieval Music Manuscripts Using Stochastic Block Models
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Citations
0
Authors
2
Topic labels
3
Source readout
Source and corpus status
Venue
Transactions of the International Society for Music Information Retrieval
Source slug
tismir
Corpus placement
Core corpus
Similarity rows
Not available yet
Ranking readout
Where this paper lands in the current run
Run shadow-generalization-product-candidate-ranking-v1Top 50 surfaced
This block uses the same resolved ranking run as Recommended. Ranks here are materialized paper_scores ranks; live Emerging may be reordered by the bounded ML scorer. Family rank is global within each family, but rank is only shown when this paper lands inside the surfaced top 50.
Families present
3
Top 50
3
Run label
shadow-generalization-product-candidate-ranking-v1
Snapshot
source-snapshot-shadow-generalization-v1-20260521
Scope: family global | run rank-83787b91ef
Emerging
In top 50 at rank 27
Emerging: embedding slice fit vs included-corpus centroid (title+abstract), plus citation velocity and topic growth; not universal relevance. Bridge signal not used here.
Signals: semantic=0.8138, citation_velocity=0.0000, topic_growth=0.9420, diversity_penalty=0.0000
Why this surfaced | 3 used | 1 penalty | 1 not computed
Embedding slice fit (corpus centroid)used
Embedding slice fit (corpus centroid): high; used in final ranking (contribution to score: 0.1628)
Recent attentionused
Recent attention: low; used in final ranking (contribution to score: 0.0000)
Topic momentumused
Topic momentum: high; used in final ranking (contribution to score: 0.2826)
Cross-cluster signalnot computed
Cross-cluster signal: not computed for this run
Similarity penaltypenalty
Similarity penalty: reduces score when non-zero (contribution to score: 0.0000)
Bridge
In top 50 at rank 7
Multi-topic paper in active topics; no cluster_version on this run so bridge_score was not computed.
Signals: citation_velocity=0.0000, topic_growth=0.9420, diversity_penalty=0.0000
Why this surfaced | 2 used | 1 penalty | 2 not computed
Semantic matchnot computed
Semantic match: not computed for this run
Recent attentionused
Recent attention: low; used in final ranking (contribution to score: 0.0000)
Topic momentumused
Topic momentum: high; used in final ranking (contribution to score: 0.6123)
Cross-cluster signalnot computed
Cross-cluster signal: not computed for this run
Topic breadth penaltypenalty
Topic breadth penalty: reduces score when non-zero (contribution to score: 0.0000)
Under-cited
In top 50 at rank 3
Low-cite candidate pool (see docs/candidate-pool-low-cite.md v0): core corpus, recency floor, citation ceiling, title+abstract gate; popularity penalty among pool members only. Semantic and bridge not yet modeled.
Signals: citation_velocity=0.0000, topic_growth=0.9420, diversity_penalty=0.0000
Why this surfaced | 2 used | 1 penalty | 2 not computed
Semantic matchnot computed
Semantic match: not computed for this run
Recent attentionused
Recent attention: low; used in final ranking (contribution to score: 0.0000)
Topic momentumused
Topic momentum: high; used in final ranking (contribution to score: 0.6594)
Cross-cluster signalnot computed
Cross-cluster signal: not computed for this run
Pool popularity penaltypenalty
Pool popularity penalty: reduces score when non-zero (contribution to score: 0.0000)
Abstract
What can we learn from the content of surviving medieval music manuscripts about their cultural context? Our study maps the spread of so‑called trope elements (textual- musical insertions into Gregorian chant) in manuscripts across medieval central Europe, specifically the Carolingian Empire and the Italian Peninsula, from the 9th to the 14th century. Using a nested stochastic block model, we infer groupings from the bipartite network of trope element occurrences in manuscripts. We identify three cohesive manuscript groups that align with the geographical borders established by the Treaty of Verdun in 843 CE. This correspondence indicates that the post‑Verdun political boundaries both channeled and limited musical exchange, along with possibly cultural transmission more broadly. Our results demonstrate how data‑driven methods can be used to evaluate assertions in prior historical work empirically. Fitting sophisticated network models to large, high‑quality datasets yields a bird's‑eye view that integrates statistical precision with scholarly interpretability, illustrating the power of computational methods for historical musicology.
Authors
Neighborhood labels
Topics
3 labels
Topic labels are imported metadata and can be noisy; use them as coarse navigation hints, not authoritative classifications.
Authorship Attribution and ProfilingDigital Humanities and ScholarshipMusic and Audio Processing
Neighbor surface
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