How the West Was Lost: The Decline of Norse Settlements in Greenland
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".