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Record W7057309463

How the West Was Lost: The Decline of Norse Settlements in Greenland

2025· other· en· W7057309463 on OpenAlexaboutno aff

Bibliographic record

VenueSkemman · 2025
Typeother
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsHuman settlementSubsistence agricultureAbandonment (legal)Settlement (finance)Subsistence economyClimate changeViking Age
DOInot available

Abstract

fetched live from OpenAlex

When the Norse arrived in Greenland they brought from Iceland their way of life, their farming methods and their worldviews. For nearly 500 years, they lived in two territories called the Western Settlement and the Eastern Settlement, which have been excavated since the early 1900s. Eventually, around the 15th century, the Norse settlements in Greenland would be abandoned. For a long time, the language used when talking of this historical event has been one of “collapse” and “demise.” Yet, all signs point to the Norse living in Greenland as they had years before in Iceland and Norway. Moreover, new research has put into question the deterministic views of earlier scholars who sought reasons behind the abandonment of the settlements in major causes such as climate and Inuit invasions. Based on the most recent archaeological and historical research, this thesis proposes that we should not talk of a societal collapse when speaking of the Norse in Greenland. Instead, the eventual abandonment was the cause of a slow trickle of events and factors like the inability of the Norse to create lasting and meaningful relationships with the Inuit’s who could have proved worthy allies in the difficult landscape of Greenland, their subsistence economy which even when changed did not answer all their needs, the climate which made travel more perilous and finally the economic isolation which was a result of changes in the demand for high-quality goods.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.175
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.1750.000

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 head0.013
GPT teacher head0.268
Teacher spread0.255 · 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
GenreOther

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
Published2025
Admission routes1
Has abstractyes

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