A Comparison Of Irish Surnames In The United States With Those Of Eire
Bibliographic record
Abstract
AbstractThis paper compares contemporary frequency distributions of Irish Surnames in Eire (2001) and the United States (US) (1997), about one hundred years after bulk of Irish emigration to the US, in order to measure changes, if any, in form and frequency of these surnames.The Eire Data (ED) source is taken from the Eire 2002 Electoral Roll, where the graph of population against surnames is shown to be typical. The US Data (USD) source is Hanks’ Dictionary of American Family Names (DAFN). Results of a first comparison of these two sources prompted removal from the USD of all Irish surnames that also have UK roots, including 33 of the 100 most frequent surnames in the Eire data. A second comparison shows that many US surnames of Irish origin are not present in Eire: these are variants of common Irish surnames, and were then merged with the etymological Irish form. The remaining 67 of the most frequent 100 surnames from ED were then compared with USD. All except one are of roughly comparable frequency order albeit with some changes to their spelling form. It is concluded that the US Irish surnames clearly reflect their heritage although some are have never been found in Eire.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".