Non-binary language in consecutive interpreting from English into Italian: An experimental study on the viability of schwa endings
Bibliographic record
Abstract
Italian is a gender-marked binary language with masculine and feminine as the only possible grammatical genders. One strategy that was recently put forward to deconstruct the gender dichotomy and to allow for non-binary individuals to be linguistically acknowledged is to replace gendered endings (-a,-o,-e,-i) with a schwa (ə), both in writing and speech. While some authors and media outlets have started adopting this solution in written texts, questions may arise concerning its viability in the oral medium, particularly in constrained translation modes such as conference interpreting. If, on the one hand, the schwa could serve as a less time-consuming alternative to extended gender-neutral periphrases, on the other, such benefit may be outweighed by processing capacity overloads, since non-binary schwa declensions entail morphological changes that are non-standard in spoken Italian, and may thus prove too burdensome to control. In order to investigate this issue, an experimental study was conducted with 12 advanced trainee interpreters who were instructed to interpret consecutively a text from English into Italian using schwa endings when referring to humans. Although with a certain amount of non-fluencies, misuse of schwa endings and occasional relapse into binary language, participants were mostly able to implement effective non-binary language strategies.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".