Translating Gjergj Fishta's epic masterpiece, Lahuta e Malcis, into English as The Highland Lute
Bibliographic record
Abstract
The Highland Lute, the Albanian national epic poem, contains 15,613 lines. It mirrors Albania’s difficult struggle for freedom and independence which was finally achieved in 1912. It was important for Robert Elsie and I to achieve an atmosphere similar to that of other important European epics such as Beowulf (England), The Kalevala (Finland), and the grand medieval poems of the eleventh and twelfth centuries such as The Song of Roland (France), Nibelungenlied (Germany), and Poem of the Cid (Spain). Rhythmically, The Highland Lute is very much like the American writer Henry Wadsworth Longfellow's epic poem, Hiawatha, parts of which I loved to recite as a young girl. Our task with translating The Highland Lute into English has been to make the language relevant and understandable for the modern reader while still retaining its colloquial, archaic, majestic, and heroic feel which gives a strong sense of the past. Quite a challenge! We translated many expressions unique to Gheg, and did our best to describe symbols of Albanian mythology and legend such as oras (female spirits), zanas (protective mountain spirits), draguas (semi-human figures with supernatural powers), shtrigas (witches), lugats (vampires), and kulshedras (seven-headed dragon-like creatures). We kept the octosyllabic rhythm consistent throughout, and we captured the qualities common to all epics: alliteration, assonance, repetition, hyperbole, metaphor, archaic figures of speech, concrete descriptions, colour, drama, passion, a range of emotions, intensity, sensuality, lots of action, rhyme where possible, and an exalted, dignified tone.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.006 |
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".