Auditioning for the Role of a Lifetime: Performing Self-Translation at the American Immigration and Naturalization Service
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.’
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it