Translation and Adaptation Studies: More Interdisciplinary Reflections on Theories of Definition and Categorization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it