Bibliographic record
Abstract
Daniel Simeoni’s call for an actor-based complement to the concept of norms in Translation Studies and its subsequent introduction of the habitus concept has revealed groundbreaking. Among other things, Translation Studies has benefited from using habitus as a conceptual tool to comprehend the translator/interpreter as a professional. However, as already pointed out by Simeoni 1998, a translator’s habitus cannot be reduced to his/her professional expertise as a translator. The present essay takes this observation a step further and argues that a translator’s plural and dynamic habitus (Lahire, 2004) also stands for a socialized individual with various positions and perceptions in other fields (e.g. the literary field for a literary translator especially when he/she is a novelist or critic him/herself) of which it would be artificial to isolate the translatorial habitus. A nuanced understanding of literary translators’ self-images and roles in cultural history asks for fine-grained analyses of their dynamic and plural intercultural habitus in all its complexities. It will lay bare translators’ multipositionality across linguistic, national and field-specific boundaries and the perceived aims, forms and functions of their multiple transfer activities, e.g. for the establishing of a national or international culture. Such an analysis may also contribute to a renewed model for interdisciplinary and intercultural historiographies of culture embedding translation within a multitude of transfer activities (translation, self-translation, etc.). As an illustration hereof, this essay analyzes a literary translator’s habitus in early 20th century Belgium.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.037 |
| Scholarly communication | 0.020 | 0.022 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".