Bibliographic record
Abstract
In its broadest sense, politics is "the ability of a society (a political com mu nity) to ask questions, to formulate short-lived responses, and to invent a series of unsatisfactory connections to bind toge ther its diverse segments" (Houle and Thériault, 2001, p. 66; our trans.).Binding together different political ideas is, of course, about power strug gles, which are at the heart of politics, but it also relates to medi ation, a concept fruitful in both political science (Kydd, 2003;Böhmelt, 2011;Ramirez, 2017) and translation studies (Bedeker and Feinauer, 2009;Bassnett, 2011;Liddicoat, 2016).In translation studies more particularly, the mediation of diverse cultural or ideo logical perspectives has been approached from various angles.For instance, Basil Hatim and Ian Mason, in their classic The Translator as Communicator, have used mediation from a discursive and textual point of view, where translators "intervene in the transfer process, feeding their own knowledge and beliefs into the processing of text" (1997, p. 147).For them, the translation of ideologies becomes a matter of me di ation, in greater or lesser degrees.Other translation scholars have used the concept of mediation from a broader and more global position, such as Maria Tymoczko, who posits that translators are among "the chief meditators between cultures" (2009, p. 184).In any case, the role of translation and the role of translators is never neutral, and the relation between translation and politics is multifaceted and of great interest to professionals, scholars, politicians, and the general public.Policies, like politics, are wide-ranging: as María Sierra Córdoba Serrano and Oscar Diaz Fouces note, public institutions develop poli cies-or public interventions and decision-making responses-to
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.849 | 0.721 |
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".