Bibliographic record
Abstract
E rika Gottlieb has probed the depths of a troubled inheritance and a bal- ancing grace in Becoming My Mother's Daughter.Between the ravages of the Holocaust in Budapest, Hungary, where she and her family were born, and the treasures she discovers in her mother 's old handbag in Montreal, Canada, Gottlieb's journey traverses lands, languages, and seas in order to declare: I mourn, therefore I am.It is in the mourning, Gottlieb concludes, that her life continues.She uses the allegory of the streetcar and its underground city routes at important intervals in her story: the narrator must choose to climb aboard the streetcar when the conductor beckons her, and on this vehicle she must travel through the tunnels in order to come out the other end.Her journey represents the stages of Eva's becoming Eliza.Becoming My Mother's Daughter is truly a story of survival and renewal, but it is also a story of malaszt, 1 or grace.In 1944, death is all around the family and their friends, all around Budapest, a city that lost the majority of its Jewish population in the last few months of the war.As László Karsai tells us, "according to conservative estimates the number of Jews in Hungary on October 15, 1944, was approximately 300,000.Most lived in Budapest, and many were at work in the countryside as forced labor servicemen." 2 Eva and her mother are among the numbers in Budapest, and Eva's father, Stephen, is one of many Jewish Hungarian servicemen forced to work in the labour battalions.vii
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.816 | 0.812 |
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".