Bibliographic record
Abstract
Let's scrutinize that idyllic snapshot of privilege and belonging.My family history is a patchwork of grudgingly told anecdotes marred by paucity of detail.The following facts comprise most of what I know of my antecedents.My parents were the children of Jewish immigrants to Canada.My maternal grandfather arrived at the Port of Montréal via New York's Ellis Island in 1921 from Zareby Koscielne, the shtetl ("small village" in Yiddish) in Poland where the family lived.All I know about my family's life in this shtetl is that each time there was a local dispute about occupied territories, the control of the village alternated between Russia and Poland.My maternal grandmother, who worked as a bar maid at a local tavern (this is one of the very few personal details she shared with me about her life in Poland), had to switch language of daily use frequently, speaking either Russian or Polish to serve the current clientele, which was mainly comprised of soldiers.When they left Eastern Europe to avoid the worsening socio-political situation, only part of my grandmother's family could afford passage.My brother and I don't know how many siblings were left behind or if they survived the subsequent local pogroms 3 and more farreaching wars.My grandparents refused to talk about those who stayed.Those siblings who immigrated to North America were split up by the authorities at Ellis Island: one brother Figure 1: My maternal grandmother (second from left), in the local tavern where she worked.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.291 | 0.148 |
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".