Bibliographic record
Abstract
This article discusses the evidence for the journeys of several Norsemen to a place called Vínland around the year 1000. In hindsight, the stories of the unsuccessful attempt to settle Vínland have been enduringly linked to the consequent discoveries of the American continents, which occurred five centuries later. However, as there are no contemporary, or near-contemporary, written records of journeys to Vínland and the nearby islands, all reconstructions of those events spring from later texts, some of them written down 300 years or more after the fact. Yet what may, or may not, have happened has gradually been granted the status of a real event. Reevaluating the wishful reality of the Vínland islands requires that the stories of the Vínland journeys be squarely situated in the context of the world geographic system adopted by those who told those stories. This article examines how information about the newly encountered lands intersected with the dominant system of defining and classifying knowledge. It thus sheds light on the worldview, now obsolete, in which that system was embedded. A careful dissection of the narrative of the Vínland journeys makes it possible to understand the morphology of this worldview, its epistemic underpinnings, and the spell it continues to cast on the Western imagination.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.091 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".