“The glory or the story of the sea-divided Gaels. One in name, and one in fame…” : How Atlantic Newspapers Impacted the Irish
Bibliographic record
Abstract
The Great Famine was a turning point in history that led to lasting repercussions for not only the country of Ireland, but for its citizens. Destruction ran throughout Ireland and as many of the Irish immigrated to other countries they became sick with typhus along the way. With these sick immigrants coming into ports people in host countries began to panic. For example, in 1847, England and Canada experienced typhus epidemics that were immediately linked to the Irish. My paper asks how mass immigration and fears of epidemics affected the perception of Irish in mid-nineteen century Atlantic World cities? It addresses this question by analyzing how newspapers covered immigration and disease. Looking through English-language newspapers reveals the bias transmitted by the print media with regards to the Irish. Terms such as “Irish fever” and the “Celtic Plague” began to spread in the coverage of typhus. My paper argues that the mass immigration of the Irish to Canada, England, and the United States led many to fear a typhus epidemic. The reactions from the newspapers held real life consequences in regard to the Irish and immigration. The consequences ranged from religious discrimination to medical discrimination contributing to the marginalization of the Irish in these communities.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".