Science, Translation and the Mangle: A Performative Conceptualization of Scientific Translation
Bibliographic record
Abstract
Against a backdrop of growing interest in historical and sociological approaches to the translation of science, this paper explores the conceptual potential of Andrew Pickering’s ‘mangle of practice’ (Pickering 1992; 1993; 1995; Pickering and Guzik 2008) as a sociological framework for research into the translation of science. Pickering’s approach is situated within a performative idiom of science and seeks to account for the interplay of material and human agency in scientific practice. It sees scientific and technological advances as emerging temporally from a dialectic of resistance and accommodation, metaphorically the mangle of practice. This paper introduces the main tenets of Pickering’s argument, contextualizing it within the field of science and technology studies. It then explores some of the implications of construing translation in these terms. Firstly, this conceptual approach helps to recognize the role of translation in the performance of science and to seek ways of studying translation practices as an integral component of scientific practices. Secondly, Pickering’s posthumanist or decentred perspective focuses on both material and human agency and the interplay between them; a similar approach to the study of translation would foreground the interaction between translator agency and material performativity in studies of translation practices. I conclude with proposals for adopting this ontological shift in translation studies, where it may have the potential to enhance our understanding of translation practices, in particular in relation to tools, technologies and sociotechnical developments in translation.
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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 | 0.030 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.012 | 0.127 |
| Scholarly communication | 0.023 | 0.033 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.011 | 0.010 |
| 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".