‘Dare the boist’rous main': The role of the <i>Belfast News Letter</i> in the process of emigration from Ulster to North America, 1760–1800
Bibliographic record
Abstract
Before the mass migrations from Ireland in the nineteenth century, earlier waves of migration in the eighteenth century saw significant numbers of people leave Ireland, predominantly from Ulster, to settle in North America. This article, using as its principal data source the Belfast News Letter ( BNL), its letters, advertisements and reports, focuses firstly on reconstructing the late eighteenth‐century migration process and voyage, highlighting the barriers represented by the Atlantic Ocean. In addition to the challenges of the sea, there were problems with the ships, the ever‐present danger of disease and also threats from other vessels, from privateers to press gangs. The voyage was recognized as a ‘universal dread’, and the risks taken to ‘dare the boist’rous main' were perhaps not minimized in the pages of the BNL, whose editorial stance was antipathetic to the migration for the potential harm it caused to Ulster by removing so many of its industrious young. The second part of this article goes on to consider the newspaper's and others' vested interests in the emigration process, demonstrates how these were manifested in the press and sets the coverage of this very significant early emigration flow within the context of contemporary religious and colonial discourses at a period of very lively transatlantic interactions.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".