‘I like to think of my comics adaptations as my own recitations … ’: in conversation with Julian Peters
Bibliographic record
Abstract
Julian Peters is a comic book artist and illustrator living in Montreal, Canada. In the last few years, he has focused on adapting classic works of English, French and Italian literature into comics. His works have been featured in anthologies, magazines, and school textbooks. Poems to See by: A Comic Artist Interprets Great Poetry (2020) is his recently published book featuring comics adaptation of 24 classic poems. His adaptations of poems by Arthur Rimbaud and François Villon were included in The Graphic Canon (Seven Stories Press, 2012), and his adaptation of T. S. Eliot’s ‘The Love Song of J. Alfred Prufrock’ was featured in Slate Magazine. In spring 2015, he was ‘Cartoonist in Residence’ at Victoria University of Wellington, New Zealand. Julian holds a master’s degree in Art History from Concordia University and takes his creative inspiration from the great and minor masters of all artistic periods. In this interview, Julian Peters discusses his creative process, his love for classic poetry, and his comics adaptation of his beloved poems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.016 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 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".