Race, Nation, and Empire in American History. Ed. by James T. Campbell, Matthew Pratt Guterl, and Robert G. Lee. (Chapel Hill: University of North Carolina Press, 2007. viii, 383 pp. Cloth, $65.00, ISBN 978-0-8078-3127-4. Paper, $22.50, ISBN 978-0-8078-5828-8.)
Bibliographic record
Abstract
Some collections of essays are kept close by because they contain discrete essays on topics of interest. They are easy to find and grab. Others, like this book, contain essays that, gathered together, offer something new, important, and exciting. It is rare that a collection should be read cover to cover; this one should. Race, Nation, and Empire in American History is part of a recent spate of books that express the now-dated desire for the internationalization of American history through deliberate engagement with ideas of race and empire, rather than through a more vague sense of transnational context. This collection emerged from a 2003 Brown University conference on “Race, Globalization, and the New Ethnic Studies.” The book recalls the conference format with parts standing in for panels (with the same broad conceptual focus) and short papers. The papers introduce a range of individual projects, some of which have been or are near publication. The spectrum of topics—from Franz Boas in the first section to Samuel Huntington in the last, with the annexation of Hawaii and California farming in between—might at quick glance suggest disorder.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.040 | 0.015 |
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".