Bibliographic record
Abstract
Hermeneutic uncertainty is an inherent part of the art of translation, and its consequences are ineluctable features of translation products. In this article I support the claim that the teaching and practice of translation do not escape the social responsibility which resides in clearly declaring and acknowledging the existence of hermeneutic uncertainty. Investigating how Heideggerian hermeneutics led to Gadamer’s development of the concept of hermeneutic prejudice. I will show that the philosophical description of how this prejudice functions can be a useful part of the pedagogical materials presented by translation teachers, and can help students to approach ambiguous or difficult source text elements more confidently. Such hermeneutic consciousness-raising can also be applied to published translations, where it can be tested to reveal how translators have dealt with specific instances of hermeneutic uncertainty. The case studied here is a pair of terms occurring in Walter Benjamin’s Die Aufgabe des Übersetzers, chosen mainly for its ubiquitous presence in the field of translation studies. The story of how French and English translations differ in their understanding of this specific hermeneutic difficulty will be used to investigate the extent to which translators acknowledge (or ignore) the existence of hermeneutic uncertainty by allowing it to enter their translations or by discarding it from them.
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 | 0.029 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.099 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| 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".