Bibliographic record
Abstract
Norman Ravvin's Who Gets In is a compelling detective story that deciphers Canada's less than welcoming entrance regime in the 1930s.Tracing the labyrinthine journey of his Jewish grandfather's immigration from Poland to Canada and his subsequent efforts to bring his wife and children to join him, this narrative offers a glimpse into the bureaucratic confidence game that so many newcomers tested.Ravvin's wonderfully researched and imagined context intrigues, and his engrossing chronicle speaks for the many forgotten stories that survive between official lines.Ultimately a tale of how tenacity prevails over the reluctance of officialdom, it is also a profoundly kinetic story of perseverance."-Aritha van Herk, author of No Fixed Address, finalist for the 1986 Governor General's Literary Awards "In recounting his grandfather's quest to bring over his wife and children from 1930s Poland, Norman Ravvin uncovers an untold aspect of Canadian immigration history.Rich in archival reconstruction, this engaging, highly readable book is a welcome addition to Canadian Jewish history, narrating the rarely acknowledged story of Jewish Saskatchewan in the early twentieth century."-Aaron Kreuter, author of Shifting Baseline Syndrome, finalist for the 2022 Governor General's Literary Awards
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.618 | 0.379 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".