Colonial American Fitzpatrick Settlers Part I: Making Sense of One Line
Bibliographic record
Abstract
Before the turn of the 17th century the settlement of Irish in the Americas lacked permanence. Soon after, Irish came to North America and the Caribbean in a steady flow, and by the mid 18th century a flood of Irish and Scotch-Irish had settled in the Americas. The reasons for that settlement were many and varied, as were the geographic origins and lineages of those Fitzpatricks among the influx. This article provides a review of the forces that pushed and pulled Irish and Scotch-Irish to the Americas. By way of example, a single Fitzpatrick line demonstrates how messy traditional genealogy of early Colonial American Fitzpatricks can get. That messiness is due in no small part to the cut and paste functionality at websites such as ancestry.com. But by careful review of authentic historical records, caution with speculative associations, and the power of Y-DNA analysis, it is possible to untangle the mess and bring back some much-needed clarity. In this article, the example used is that of the well-known colonial-settler William Fitzpatrick (born ca. 1690 AD), of Albemarle County, Virginia, who arrived in North American ca. 1728. Two living ancestors of William have been found to share a common ancestry from ca. 1650 AD — both bear a genetic mutation (FT15113) specific to William's line; this enables the ready identification of male descendants of William.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".