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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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