Bibliographic record
Abstract
On May 1, 1934, when I was a month short of my sixth birthday, my parents, my older brother, and I came to New York City on the S.S. Westernland to escape from the Holocaust. My parents had both born in the late 1890s in a shtetl in Poland but they had lived in Germany for over 20 years. The dictionary says that to immigrate is “to come into a new country, region or environment, especially in order to settle there.” The operative word for me in that definition is “new.” To immigrate is to come to a new country and have new experiences. And, like everything worthwhile in life, to be an immigrant is both a blessing and a curse. It’s a blessing because it’s challenging and exciting to do something new, something different, something everyone else isn’t doing. It’s a curse because it’s scary to embark on any new activity. So to be an immigrant is to be continually caught in the tension of the excitement of being an outsider to a society and the stigma of being different from those around you. To be an immigrant Continued on page 14
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.037 | 0.022 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".