Bibliographic record
Abstract
David Bezmozgis's remarkable stories have already been acclaimed in the US and Canada when three appeared almost simultaneously in the New Yorker, Harper's and Zoetrope. In the space of a few weeks, these magazines introduced readers to the Bermans - Bella and Roman and their son Mark - Russian Jews who have fled the Riga of Brezhnev for Toronto, the city of their dreams. Natasha brings the Bermans - and the Russian Jewish enclaves of Toronto - to life in stories full of big, desperate, utterly believable consequence. In 'Tapka', six-year-old Mark's first experiments in English bring ruin and near tragedy to the neighbours upstairs. In 'Roman Berman, Massage Therapist', Roman and Bella stake all their hopes for Roman's business on their first, humiliating dinner with a North American family. In the title story, a stark, funny anatomy of first love, we witness Mark's sexual awakening at the hands of his fourteen-year-old cousin, a new immigrant from the New Russia. Bezmozgis writes with clarity and compassion about the pains and joys of immigration. Sad but comic, his stories are the literature of an immigrant community whose story has yet to be told, and their chronicler possesses an extraordinary gift.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.022 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.063 | 0.013 |
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".