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Enregistrement 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 sur OpenAlexaboutno aff

Notice bibliographique

RevueDigital Commons - Andrews University (Andrews University) · 2018
Typearticle
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueDiverse Scientific and Economic Studies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésNorwegianSettlement (finance)ProsopographyHuman settlement
DOInon disponible

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,782
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,001
Communication savante0,0000,001
Science ouverte0,0020,001
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,027
Tête enseignante GPT0,199
Écart entre enseignants0,172 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2018
Routes d'admission1
Résumé présentoui

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