An annotated translation: Natural Forms, in: Ethan Matt Kavaler, Renaissance Gothic. Architecture and the Arts in Northern Europe 1470 - 1540. New Haven and London: Yale University Press, 2012, pp. 199-229. (ISBN 978-0-300-16792-4)
Bibliographic record
Abstract
(in English): The purpose of this bachelor thesis is to translate a part of the chapter Natural Forms from the book Renaissance Gothic. Architecture and the Arts in Northern Europe 1470 - 1540 written by Ethan Matt Kavaler, a Canadian professor of art history. In the commentary that follows the translation the author considers the specific factors that have influenced the genesis of the original. Then, she analyses its lexical and stylistic features and characterizes the text as an essay of considerable qualities. Drawing upon the analysis the author describes the translation problems and the translator's strategies employed to solve them. She attempts to identify the translation shifts and discusses the reasons of their occurrence. It is observed that the translation tends to be more explicit, uses more variable language solutions and some solutions of increased expressive intensity. This tendency is consistent with the effort to adapt the translation to the target language culture and to preserve the quality of the original.
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 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.054 | 0.012 |
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".