Bibliographic record
Abstract
Post 9/11, immigration looms large in concerns about national security. Today, visitors, if not immigrants, conjure up specters of physical and bio-terrorism and the theft of jobs. Written before and without reference to current debates and concerns, Amy L. Fair-child's Science at the Borders is nonetheless a reminder of America's long history of engagement with immigration. Through detailed analysis of numbers, combined originally with Foucaultian theory, Fairchild traces changes in the immigrant medical examination to make nuanced and sophisticated arguments about “science and power—of the power of an industrial mindset to penetrate science and make the immigrant medical exam into a tool for defining and shaping the nation's laboring classes” (p. 16). Fairchild's material and analysis make novel and important contributions to immigration history. The book is based on extensive quantitative analysis of immigration records. Rich first-person narratives personalize the numbers. Fairchild clearly enjoyed playing with the numbers and letting them define both the statistical story and the lines of her argument. She uses the term line often and creatively to refer at once to the graphs resulting from statistical analysis, the discipline of the industrial assembly line, and the social and racial divisions among immigrants (p. 86). In this spirit, Fairchild divides her book into two sections. The first, “Numbers Large,” examines the big picture of immigration inspection as an inclusionary tool that the United States used to build its labor force and economic power. Despite the title “Numbers Large,” this section is as much theoretical as statistical, and it uses the personal tales of the numerical majority repeatedly and forcefully to remind us that medical inspection did not exclude most immigrants from entry. The “overarching purposes of the exam,” she argues, “was to control rather than to exclude” (p. 106). It helped import and produce healthy and appropriate workers.
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 | 0.001 | 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 | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.260 | 0.131 |
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".