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‘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

2006· article· en· W2141706367 on OpenAlexvenueno aff
Stephen A. Royle, Caitríona Ní Laoire

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicIrish and British Studies
Canadian institutionsnot available
Fundersnot available
KeywordsEmigrationNewspaperContext (archaeology)HistoryColonialismHarmLawSociologyMedia studiesPolitical scienceArchaeology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.218
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.007
Science and technology studies0.0020.004
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.004
GPT teacher head0.189
Teacher spread0.185 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2006
Admission routes1
Has abstractyes

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