Sarah Howe Interviews #GriffinPoetryPrize International Finalists Ali Kinsella & Dzvinia Orlowsky
Bibliographic record
Abstract
"Each line [in Natalka Bilotserkivets' poetry] is very powerful, and each line turns the poem in a different direction." —Dzvinia OrlowskyIn this interview with Griffin Trustee Sarah Howe, 2022 #GriffinPoetryPrize International finalists Ali Kinsella and Dzvinia Orlowsky discuss what drew them to translate the work of Natalka Bilotserkivets, how they collaborated on their translation, and how it feels for this work to be reaching readers in English at a turbulent time for Ukraine. They also share a bilingual reading of the poem "ніж / Knife" from their shortlisted translation, Eccentric Days of Hope and Sorrow (Lost Horse Press).Join us on our website and YouTube channel on Wednesday, June 15, 2022 at 10:00 a.m. (ET) for the live screening of our 2022 Griffin Poetry Prize winners announcement film. Find out more about this year's International and Canadian shortlists on our website: https://griffinpoetryprize.com/shortlists/prize-year/2022/
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.072 | 0.011 |
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".