Translation and Adaptation Studies: More Interdisciplinary Reflections on Theories of Definition and Categorization
Bibliographic record
Abstract
This paper discusses how theories of definition and probabilistic theories of categorization could help distinguish between translation and (literary film) adaptation, and eventually between translation (TS) and (literary film) adaptation studies (LFAS). Part I suggests readopting the common parlance definition of “translation” as the accurate rendition of the meaning of a verbal expression in another natural language, and “adaptation” as change that leads to better fit. Readopting these common parlance definitions entails categorical implications. The author discusses three parameters: whereas “translation” represents an invariance-oriented, semiotically invested, cross-lingual phenomenon, “adaptation” refers to a variance-oriented phenomenon, which is not semiotically invested, and entails better fit. Part II discusses how theories of categorization could help distinguish between TS and LFAS. The study of the disciplinarization of knowledge involves epistemic and socio-political conditioners. This section concludes that medium specificity, i.e., the linguistic versus lit-film paradigm, plays a major role in separating TS from LFAS. Another player that deserves more attention is the Romantic as opposed to the Classicist value system.
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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 | 0.024 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.006 | 0.093 |
| Scholarly communication | 0.015 | 0.036 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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".