On Translating the Fairy Tale: The Wording and Wonder of Translating Fernán Caballero’s Bella-Flor
Bibliographic record
Abstract
Following in the footsteps of the Grim Brothers, a woman named Cecilia Francisca Josefa Böhl de Faber y Ruiz de Larrea set out to collect Andalusian folk tales under the pen name Fernán Caballero. Caballero was one of the first people to record folk tales—specifically those deriving from Spain—in writing, thus helping to shape the subsequent fairy tale genre that is ever-pervasive in modern-day society. However, while many translations have been derived from the collections of the Grimm Brothers and other male collectors, Caballero´s have received less attention from English translators. One notable exception to this rule can be found in the works of John H. Ingram, who translated one of Caballero’s folk tale anthologies that included the story Bella-Flor, a Spanish folk tale about the importance of (and ultimate triumph resulting from) being good. This paper analyzes the merit of Ingram’s translation through assessing both linguistic choices and cultural edits. Analyzing this specific translation seeks to contribute to the aim of discussing the wider issues of translating stories the fairy tale genre—specifically those less notorious in modern-day Western culture—as well as the linguistic and literary choices that must be made when translating works across time and space.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".