Why complexity might not be too simple for translation studies
Bibliographic record
Abstract
This article reflects on the authors’ work in complexity thinking in translation studies against the background of current debates in the field. It argues that complexity thinking offers better solutions to translation studies than do linear and reductionist thinking. For instance, in a complexity framework, empirical and conceptual work are not seen in opposition but as mutually enriching perspectives. Equally, reducing the ambit of complexity thinking to probabilism limits the scope of solutions to complex problems. The goals of research are complex, and the tensions between various goals should not be reduced. The article also addresses the complex relationship between ontology and epistemology, arguing against a reduction to either. Lastly, it engages with particular criticism about complexity as an epistemological position and about the political intent of the authors. The article closes with an overview of the contributions that complexity thinking has brought to translation studies.
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.
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 | 0.090 | 0.196 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.045 |
| Scholarly communication | 0.019 | 0.046 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".