Literary Barters: A Network Science Approach to Agency in Translation
Bibliographic record
Abstract
This essay proposes complexity and computational network analysis as fitting paradigm and methodology for studying contemporary literary translators’ agency. Grounded in the rhizomatic structure of networks, this approach unearths the importance of translation-based literary barters for the robustness and stability of a translation sub-system, in our case the sub-system of contemporary poetry translation from American and Canadian English into Romanian. Using a mixed- method approach that combines close reading (qualitative analysis) and distant reading (quantitative analysis), the research shows that translators possess an essentially connective mind and that their own interests and network of personal connections are salient in starting and maintaining a substantial exchange of inter-cultural transfers in a transnational context. Complexity thinking provides the premises for demonstrating that translation is highly sensitive to its initial conditions of production, thus is reliant on translators, and the computational network analyses prove consequential for documenting the role of translators in initiating and carrying out literary translation projects.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".