Translation in Medieval and Reformation Norway: A History of Stories or the Story of History
Bibliographic record
Abstract
Three major events marked medieval Norwegian literary production, style, and language: the introduction of Christianity, the Black Death, and the Reformation. Foreign material in translation was pivotal to the transition between the pagan Viking Era and the Christian Middle Ages and to the passage from Catholicism to Lutheranism in the 16th century. Lack of translation and literary production following the Black Death also had an impact. Translation in a medieval and Renaissance context must be understood as transfer of knowledge, the crossing of linguistic and cultural borders. The translated texts helped introduce and consolidate the social conventions promoted by the new religion. The distinction between story and history faded. Religious and devotional material preceded the secular court literature from the French-speaking territories. Hagiographic material ran parallel to heroic tales: all genres helped illustrate the virtues of Christian life and social organization and needed only minor adaptation for a Norse audience. The pagan literary conventions blended with those of the imported material and resulted in a distinct Norse literary style. The systematic encounter with other gave rise to a new perception of self. The largely anonymous translators contributed to the inclusion of other in self, to the assimilation of foreign cultural values and concepts.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
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".