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Colonial American Fitzpatrick Settlers Part I: Making Sense of One Line

2020· article· en· W3108526126 on OpenAlexaff
Ian Fitzpatrick

Bibliographic record

VenueJournal of the Fitzpatrick Clan Society · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicArchaeology and Natural History
Canadian institutionsCanadian Rheumatology Association
Fundersnot available
KeywordsIrishColonialismGenealogyHistorySettlement (finance)Genetic genealogyCLARITYPower (physics)EthnologyGold rushArchaeologyDemographySociologyPopulationBiology

Abstract

fetched live from OpenAlex

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.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.319
Threshold uncertainty score0.503

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.039
GPT teacher head0.303
Teacher spread0.264 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
Published2020
Admission routes1
Has abstractyes

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