Expanding norm theory in TIS: Proposal for a framework of translator norms
Bibliographic record
Abstract
This article is comprised of a rationale and proposal for a framework of “translator norms,” intended to extend and complement the existing norm frameworks in Translation Studies. The proposed concept of translator norms focuses on normative behaviors and expectations around the figures of translators as individuals, rather than on the process and product aspects that have predominantly been the focus of existing work on translation norms. Translator norms, as described here, tend to logically precede the work of translation and often relate to the hiring and selection of translators. The categories proposed are discussed with reference to existing scholarship in Translation and Interpreting Studies that highlights the kinds of tendencies and values that can be interpreted in terms of norms. While the norms themselves are not the focus of the article, some specific norms are discussed in the elaboration of the categories within which we might investigate them and others. Retheorizing these areas as part of a structure of translator norms can help promote critical evaluation of assumptions and implications of particular decisions around translators that may turn out to be context-specific and normative rather than universal. A re-evaluation of some of these assumptions can in turn facilitate decolonizing approaches in translation and Translation Studies by calling attention to previously devalued practices, identities, and forms of knowledge.
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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 | 0.106 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.008 | 0.084 |
| Scholarly communication | 0.013 | 0.031 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".