Bibliographic record
Abstract
HHHHHHHHHHHHHHHHHHHHHHHHHHwas taller than all my grandparents," said my mother, Rose Epstein Wodlinger, who was five feet tall in a stretch.That, and the brief, chilling incident that opens this narrative, plus the fact that they were all born in European countries-Russia, Germany, Lithuania for sure-and one, more exotic greatgrandmother, in Sophia (whether under Turkish or Greek domination at the time is unclear), represents all I know about my ancestors.One other fact: Baba Rosen (see picture) and Zada Rosen (no picture, but I remember him from my childhood) both died in Winnipeg and were survived by six daughters and two sons.What happened in their lives from birth to death (aside from successful coupling!)I'll never know; there's no one left to tell me; no written record, nothing.Of the others, I know even less.Not wanting you, my heirs, to be deprived of a past history, I've assembled this collection of family memories for your edification.It's all very personal and may tell you more than you care to know about Drainies and Wodlingers and Epsteins (if I've left anything dangling consult your parents)-but I've written truthfully, and with affection.I offer it to you as a gift-not with humility, but with pride: pride in your interest in reading it several generations removed-and pride in myself for making the effort to write and complete it: I never thought I would!XI " I
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 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.532 | 0.361 |
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".