Bibliographic record
Abstract
Riassunto : Il saggio analizza le attuali tendenze d’uso per i femminili di professione e di carica, con particolare riguardo alle ideologie linguistiche e all’autorappresentazione linguistica delle donne, presentando sia i riscontri di sondaggi sulla scrittura giornalistica e sulla scrittura estremamente varia di Internet sia i dati raccolti mediante un questionario sul linguaggio di genere. Particolare attenzione è dedicata al processo in atto di connotazione politica, sempre più forte, nell’ideologia linguistica sul linguaggio di genere: l’opposizione ai nomi femminili di cariche, propugnata già in passato da esponenti della classe politica di centrodestra, si configura oggi alla stregua di un tratto identitario di tale area politica.||Abstract : This paper analyses current usage trends for feminine forms indicating professions and roles held by women in Italian, paying particular attention to linguistic ideologies and women’s linguistic self-representation. It presents results of surveys about the journalistic writing and the extremely varied writing on the Internet, as well as data gathered from a questionnaire about gender-inclusive language. Special emphasis is devoted to the ongoing process of increasingly strong political connotation in the linguistic ideology of gender language: the opposition to feminine job titles, advocated in the past by members of the centre-right political class, appears now as a feature of this political identity.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".