Meet, Greet, Translate: Mapping Happenstances and Network-Driven Translations in Contemporary Literary Transfers
Bibliographic record
Abstract
This essay explores the role played by randomness in contemporary poetry translation. I argue that translation happenstance—an instance of cultural transfer that is not part of a pattern and is unlikely to replicate—is a useful concept that explains the decentralized, highly sinuous, and unpredictable context of poetry translation, especially in small, non-hegemonic countries. Happenstances may be one-time occurrences or may evolve into network-driven translations—transfers in which an individual’s circle of friends and acquaintances play a mediation role and which develop according to the agents that join the network. Burrowing into the nooks and cranes of printed periodical publications in Romania between 2007 and 2017, this contribution uses a mixed-method approach to investigate computationally (via distant reading) and via close reading the network of contemporary poets, translators, and publications that engaged in a sustained reciprocal translation dialogue with the United States and Canada and concludes that agent-based network models of historical and bibliographic resources are needed in order to account for the complexity of any literary translation act.
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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 | 0.006 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".