Bibliographic record
Abstract
What was the first museum you remember visiting? I was born in September 1942 during the war. My parents came from Poland. Three weeks after I was born, 6,500 Jews from my father’s hometown, Opatów (Apt, in Yiddish), 65% of the population, disappeared overnight. All but 500 were sent to the Treblinka death camp, and the rest to a forced labour camp. So I grew up in an immigrant neighbourhood in the immediate postwar years. I went through an ultra-Orthodox period (my parents were horrified). I became not only strictly kosher, but also I observed the Sabbath very strictly. That meant I could not ride, spend money, turn on the radio, write, tear paper . . . I could do almost nothing. Except . . . I could walk to the Royal Ontario Museum. . . . and I did. So this was before the era of helicopter parents. At the age of 10, 11, 12 years old, I would walk out of my house, through Queen’s Park, to the ROM, and that was my beloved childhood museum.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.499 | 0.226 |
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".