Translation, Systems and Research: The Contribution of Polysystem Studies to Translation Studies
Bibliographic record
Abstract
Translation, Systems and Research: The Contribution of Polysystem Studies to Translation Studies — The aim of this article is not at all to examine Polysystems theory nor Polysystems research as such, but rather to discuss the impact Polysystems research has had in the development of a new discipline, i.e. Translation Studies. The ambiguous position of PS research within Translation Studies is due to its interdisciplinary claims and, on the other hand, to the necessity to work in a real world of disciplines where institutionalization is inevitable and even needed. The starting point of PS theory is not translation at all, but rather the dynamic functions fulfilled by translation within (inevitably) heterogeneous cultures and societies. On the basis of such hypotheses about culture(s) a rich panorama of new questions for research on translation has been worked out, as well as methodological models, and individual and collective descriptive research has been started in many countries on many cultural situations. Hence it may be accepted that descriptive research on translation would hardly have existed without the programmatic PS contribution and that the establishment of Translation Studies as an academic discipline is greatly indebted to PS. The gradual extension through various countries and disciplines (film studies, media studies, social organization, etc.) has favoured combinations with other approaches while making less clear the specific profile of the PS approach. It may be said that PS has served research as such, much more than its own sake, but wasn't this exactly the goal it wanted to achieve?
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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 | 0.048 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.007 | 0.067 |
| Scholarly communication | 0.016 | 0.031 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".