Sulla proposta di creazione di un nuovo genere in italiano : riflessioni, problemi, simboli
Bibliographic record
Abstract
Riassunto : L’articolo affronta la questione del genere grammaticale nel dibattito contemporaneo riguardante l’italiano, evidenziando una connessione tra la questione dell’uso sessista della lingua – in particolare del maschile sovraesteso – e la proposta di creazione di un nuovo genere con morfemi flessionali -? (singolare) e -3 (plurale) per favorire l’inclusività dei soggetti sessualmente non-binary. Tale cambiamento sarebbe di grande impatto sulla ristrutturazione morfologica dell’italiano e per questo si è aperto un accesissimo dibattito, ancora in corso, di cui qui si dà conto. Nell’ultima parte del contributo si presentano i risultati di una prima ricognizione volta a sondare la conoscenza di -? e -3 in un campione di utenti.||Abstract : The paper addresses the issue of grammatical gender in the contemporary debate about Italian Language, highlighting a connection between the issue of sexism – rapresented by the use of masculine gender – and the recent proposal to create a new gender, using the morphemes -? (singular) and -3 (plural), referred to non-binary people. The change would have a great impact on morphology of Italian Language and for this reason it has raised a very heated debate which is still ongoing and reported here. In the last part of the paper is reported the result of a survey on the knowlegde of -? e -3 in users.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".