Interpreting Dragomans: Boundaries and Crossings in the Early Modern Mediterranean
Bibliographic record
Abstract
Early modern observers rarely failed to comment on the perceived diversity of peoples, customs, and languages of Mediterranean societies. This diversity they sought to capture in travel narratives, costume albums, missionary and diplomatic reports, bilingual dictionaries, and a range of other genres of the “contact zone.” Modern scholars, too, have celebrated the early modern Mediterranean's ostensibly multiple, diverse, and even “pluralist,” “cosmopolitan,” or “multicultural” nature. At the same time, in part thanks to the reawakened interest in Braudel's seminal work and in part as a much-needed corrective to the politically current but analytically bankrupt paradigm of “clash of civilizations,” recent studies have also emphasized the region's “shared,” “connected,” “mixed,” “fluid,” “syncretic,” or “hybrid” sociocultural practices. Of course, these two analytical emphases are far from mutually exclusive, as recently underscored by Peregrine Horden and Nicholas Purcell's comprehensive, longue durée model of diversity-in-connectivity. Yet, neither Horden and Purcell's structuralist “new thalassology,” nor other studies of the early modern Mediterranean have offered a systematic account of how “diversity” and “connectivity” as both the flow of social practices and the categories for speaking about them have been articulated through specific institutions and genres.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.030 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".