My Great-Grandfather Danced Ballet
Bibliographic record
Abstract
“My Great-Grandfather Danced Ballet” is a poetic exploration of queer longing, queer mundanity, and contemporary cultural Judaism. The project has two primary components: (1) epistolary, persona, and narrative poems that tell the story of Ernest and Rubin, two homosexual Jewish lovers in pre-Holocaust Romania, and of Ernest’s later life in mid-twentieth-century Montreal; and (2) prose, lyric, and form poems that explore my homonormative existence in contemporary Montreal, including my relationship to my cultural Judaism, my romantic engagement(s), and my material comforts. These two components are connected by the fact that Ernest is a heavily fictionalized version of my actual maternal great-grandfather, Herbert, about whom I know very little save for the fact that he danced ballet in Romania before immigrating to Montreal. The two components intersect in metapoems that explore my queer longing: my desire to discover that my homosexuality, my queerness, is shared with a member of my family. The project is inspired by other contemporary texts, such as Casey Plett’s Little Fish and Lisa Richter’s Nautilus and Bone, that also seek to reposition familial or Jewish history in a queer context, and by media such as Bianca Stigter’s documentary Three Minutes: A Lengthening and Sebastian Meise’s drama Große Freiheit (Great Freedom), both of which explore mid-twentieth-century Judaism and/or queerness using new and innovative lenses.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".