Bibliographic record
Abstract
This chapter discusses how newspapers helped to integrate the vast British Atlantic. Reports and editorials appearing in the dozens of “Weekly Mercuries” carried by ships crisscrossing the ocean gave meaning to an emerging, shared understanding of loyalty and loyalism among the ocean's many and varied British inhabitants. This shared understanding of Britishness drew on Protestant subjects' deeply held fears of their nation's long-standing Catholic enemies, France and Spain. The makings of Daniel Fowle's “Body Politic,” however, depended on reliable Atlantic communication networks, a circulatory system capable of carrying news quickly and regularly to all corners of Britain's vast empire. News of national importance was filtered through these more immediate webs of contact, which played a significant role in shaping distinctive local political cultures and identities in places like New York City, Glasgow, Halifax, and Kingston. During the many wars fought against France and Spain in the first half of the eighteenth century, anti-Catholic rhetoric was able to overshadow divisions within the empire, providing a language of national unity that was so intentionally broad as to appeal to the nation's diverse inhabitants. But in the absence of these wars and these enemies, as was the case for much of the 1760s and 1770s, subjects in these communities struggled to understand what exactly united them as Britons.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.099 | 0.032 |
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".