Auditioning for the Role of a Lifetime: Performing Self-Translation at the American Immigration and Naturalization Service
Bibliographic record
Abstract
For all the rhapsodic invocations of Lady Liberty’s welcoming torch – and they are legion – it takes only a cursory glimpse around the Ellis Island Museum to conclude that the process of American immigration at the turn of the twentieth century was a tortuous and torturous one. Once steerage passengers made it off the boats, they had to parade past an audience of Public Health Service officers, who would screen them for symptoms of disease and disability. One such officer, known as the “eye man,” would flip their eyelids inside out with a hooked instrument to check for signs of trachoma and conjunctivitis. Another would vigilantly screen for abnormalities in gait, posture and skin condition. To weed out those considered mentally incompetent, a series of identificatory and (sometimes) mathematical questions were asked to immigrants who seemed, in the words of a surgeon and Public Health official, “inattentive and stupid-looking” (Mullan). If the candidate responded poorly to these questions, the examiner would chalk her shoulder with an X, causing her to be diverted into the “mental room” and, possibly, returned to the boat.’
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".