Bilingualism and Literary (Non-)Translation: The Case of Trieste and Its Hinterland
Bibliographic record
Abstract
This article addresses the question of weak translation activity in bilingual settings. It presents an analysis of the situation in the city of Trieste and its surroundings, where a substantial Slovene minority has lived for centuries alongside the Romance-speaking (mainly Italian) population as well as various other smaller ethnic groups. The Italian and the Slovene communities have had different histories and at various points conflicts between them have arisen, sparked by national issues and complicated further by political circumstances. To a large extent, the two ethnic groups have lived parallel lives, often showing only minimal interest in each other’s culture. This has had an impact on literary translation, the output of which has been rather modest until recently, and often even more so on the reception of translated works – in spite of the city’s rich literature in both Italian and Slovene. This article seeks to identify and explore the nature of this translational relationship, taking into account the underlying social, political, cultural, literary, and linguistic factors. It argues that the situation began to change in the early 1990s when the asymmetries between the two ethnic groups started to diminish and the Slovene culture and language gained greater recognition, which in turn opened new prospects for 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.014 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".