Bibliographic record
Abstract
Abstract The purpose of this paper is to shed some light onto contemporary translation didactics as a “virgin” field of applied TS that cannot be successfully developed without a new, multidisciplinary approach that would put forward the specificity of translator training as a distinct, socially valuable practice. We hypothesize that as socially-specific, practice-oriented products of the interaction between the systems of translation and of professional education, translator training programs are dependent on the social perception of translating activity as well as on the degree of its institutionalization as a profession. Given that contemporary translation and interpretation practices, as well as translator training programs, are limited to local manifestations, the social and cultural discrepancies impede any comparativism in this field of applied TS. However, in applying a sociological approach to translator training, we propose a methodological framework for a sociologically-informed comparative analysis that would lift the cultural and institutional barriers that until now have been distorting our vision of translation as a global social practice and have thus prevented us from conducting comparative analysis of a wide variety of translational phenomena as manifested in different locales, conceived in terms of both time and space. In order to illustrate our propositions, we present the reader with a case study of the most prototypical translator training programs in Canada and Russia – countries that, due to the differences in the theoretical, practical and didactic setup of their respective fields of translation and interpretation, offer appropriate support for our comparative methodology.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.015 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".