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Record W7011856619

The Norwegian Ancestry of Johannes (John) Larson (1886-1957); From the Bakken Subfarm, Guggedal Main Farm in Rogaland County, Norway to the Suldal Norwegian Settlement in Juneau County, Wisconsin

2018· article· en· W7011856619 on OpenAlexaboutno aff

Bibliographic record

VenueDigital Commons - Andrews University (Andrews University) · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicDiverse Scientific and Economic Studies
Canadian institutionsnot available
Fundersnot available
KeywordsNorwegianSettlement (finance)ProsopographyHuman settlement
DOInot available

Abstract

fetched live from OpenAlex

The Johannes Larson family is part of the settlement in southern Juneau County which became known as the Suldal Norwegian-American Settlement (see Onsager, Lawrence, The Juneau County Bygdebok, Digital Commons, Andrews University, https://digitalcommons.andrews.edu/pubs/146/). Suldal is a rural district in Rogaland County in western Norway. The connection with Suldal, Norway began in 1850 with the coming of Johannes Larson’s great uncle, Knut Ormson, to settle in Lindina Township, Juneau County, Wisconsin. Chain migration by kinship groups followed. In 1864, Johannes Larson’s grandfather, Lars Osmundson, a renter on the Bakken subfarm under Guggedal and a former school teacher, led a group of 50 people from Suldal. They came on two sailing ships, which left Stavanger on May 4, 1864 and arrived in Quebec, Canada on June 2, 1864. Traveling from there to Chicago, they joined relatives in Juneau County by late June of that year. By 1908, the settlement included about 500 related individuals from Upper Telemark and 1,200 related individuals from Suldal. In 1914, Johannes married Olive Onsager and founded a family of his own. This history is organized by generation and family number. The Larson family can be traced back to Oluf paa Sukka (fl. 1563 – 1618) in Suldal. Oluf is number one and his son, Aslak, second generation, is number two, etc. Because the Norwegians in Norway didn’t have set last names until about 1900, many of the early immigrants struggled to choose a name. Gerhard Naeseth, the founder of the Vesterheim Genealogy Library in Madison, Wisconsin, indexed his research to identify every Norwegian who came to America before 1850 by first name because of the difficulty in locating a person in the records by the variations of last names. For example, Bjedne Osmundson Vetrhus, an early settler in Juneau County and a Larson relative, used several names. They included variations of his first name, Bjarne, Bjorne, and Barney and variant spellings of the farm name, Vinorhus and Winterhus. He also appears in the records as Osmundson. His headstone has Bjarne Winterhus on it. Several of his children took the last name of Benson (Bjarneson). I have identified individuals by their given or first name, the given name of the father with -son or -datter added, the name of the farm on which they were born in parentheses, followed by the farm where they are living. For example, Daniel Larsson (Sukka) Herabakka (18) on page 19. However, it must be remembered that the farm name is permanently attached to the farm, not to the owner or renter. In the eighth and ninth generations, the direct paternal ancestors of Johannes Larson were renting husmann places (subfarms or cottages) on the Guggedal main farm (they were descendants of younger sons, the oldest son inherited the farm). Husmann places were with and without land. For these Larson ancestors, the subfarm is included in their name: Osmund Larsson Boen (subfarm), Guggedal (main farm) and Lars Osmundson Bakken (subfarm), Guggedal (main farm). Sometimes the immigrants used the main farm for a last name and sometimes they used the subfarm name, or they might decide to use Larson or Osmundson, etc. For those wishing to understand more about the Norwegian-American experience, please read “Community Building, Conflict, and Change, Geographic Perspectives on the Norwegian-American Experience in Frontier Wisconsin,” by Ann Marie Legreid IN Wisconsin Land and Life, The University of Wisconsin Press, 1997, edited by Robert C. Ostergren and Thomas R. Vale.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.782
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.027
GPT teacher head0.199
Teacher spread0.172 · 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.

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

Citations0
Published2018
Admission routes1
Has abstractyes

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