Constantly Translating Friulian Identity
Bibliographic record
Abstract
The purpose of this paper is to analyze the works regarding two trilingual Italian-Canadian female writers, Dôre Michelut and Mary di Michele who have chosen to concentrate on the Friulian language, either as bridge or mediator to gap the distance between cultures and languages (Michelut in “Loyalty to the Hunt” and “Ouroboros”) or as inspiration and source for translation and poetic creation (di Michele in “The Flower of Youth”). In an attempt to come to terms with my own tricultural identity I will include a few poems that I had translated from English into Friulian or vice versa.Tradurre l’identità friulana incessantementeIl presente contributo mira ad analizzare alcune opere di Dôre Michelut e Mary di Michele, due scrittrici italo-canadesi che ricorrono alla lingua friulana, da un lato come ponte per mediare la distanza tra le proprie lingue e culture (Michelut in “Loyalty to the Hunt” e “Ouroboros”) e dall’altro come fonte d’ispirazione creativa per le proprie poesie e traduzioni (di Michele in “Flower of Youth”). Nella parte finale sono incluse anche alcune poesie che l’autrice del saggio, anche lei trilingue, ha tradotto dall’inglese al friulano e vice versa al fine di venire a patti con la propria identità transculturale.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".