Bibliographic record
Abstract
Abstract The translation policy model by González Núñez ( 2013 , 475) comprises three elements, namely “translation management”, “translation practices”, and “translation beliefs”. While the first two elements of this model are straightforward and easy to study in top-down approaches, translation beliefs can relate both to policymakers and policy receivers. However, the distinction has not been clearly made in this model and the element of translation beliefs has been chiefly treated in the literature as though it comes from the top levels of policymaking, hence overlooking the bottom-up aspects of it (see González Núñez 2014 , 2016 ; Li et al. 2017 ). In order to improve this model, the present paper draws on the audience reception theory ( Hall 1973 ), and shows that the current translation policy model requires a fourth element that I would call ‘translation reception’. The paper draws on the findings of a reception-oriented case study on translation policies in provincial broadcasting in Iran. This study argues that a more inclusive model of translation policy should not only include the authority-level elements of translation management, translation practices, and translation beliefs, but also the element of translation reception on the part of policy receivers. This way, I hope, the end users’ involvement in and contribution to the translation policy network will not be overlooked in subsequent research.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.091 | 0.025 |
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".