Imaginative Networks: Tracing Connections Among Early Modern Book Dedications
Bibliographic record
Abstract
This essay uses network metrics (centrality, density, clustering coefficients) to account for shifts in dedicatory practice resulting from political crises, religious turmoil, and changes in book production practices. It constructs a network from the names that appear in dedications of EEBO-TCP texts; names are detected using the linguistic markup from the EarlyPrint project. The essay argues that we learn more about early modern book history by constructing networks of all the names that appear in dedications, not just those of authors, printers, and patrons. The network includes a mixture of religious and political figures, literary personalities, fictional characters, and bookmaking professionals, because this is the full range of names that dedicatory practice covers in the period. By proceeding in this way, network metrics can account for a range of dedicatory phenomena, including Queen Elizabeth’s popularity on both sides of the political aisle long after her death and, especially, consolidation around non-contemporary names in dedicatory practice as a result of both the Civil War and the Restoration. The imaginative networks revealed by early modern dedications are organized mainly around untimely figures from the recent and distant past, but despite this the networks are sensitive to historical change, especially at moments of political and social crisis.
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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 | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".