Rappresentare le identità di genere in una lingua che cambia: dai femminili alle identità non binarie. \nProposta di traduzione di "Alice Austen lived here".
Bibliographic record
Abstract
This thesis aims at demonstrating the importance of the relationship between language and reality, focusing on the performative aspect of language, and exploring how each person plays a role in the development of the language system. The first chapter intends to present a historical overview of the most significant and recent developments in the Italian language, highlighting how it is not the language itself to be sexist, but rather the use of language made by the linguistic community. The chapter also focuses on strategies aimed at avoiding gender discrimination as well as allowing those who do not feel represented by male or female forms to express themselves. The second chapter highlights the role of translation as a means of sharing new meanings within the linguistic reality. Starting with the contribution of the Canadian translation school and then moving to queer transfeminist translation, the chapter develops into a historical analysis of the issue, and then focuses on the strategies adopted in translation practice aimed at giving voice to marginalised identities. The focus then shifts to the difficulties concerning the translation between Italian and English, analysing some emblematic books where gender is expressed in a particular way. The third chapter is based on a more practical and personal approach, putting into practice the strategies of gender neutralisation in the translation of "Alice Austen lived here" (2022). Alex Gino’s book, centred on non-binary identities, contains sub-standard gender neutralisation strategies that can be complicated to translate into Italian, such as the use of the personal pronoun "they".
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".