Bibliographic record
Abstract
At this late date, any author who puts “race” and “empire” in the title of a book on the United States and the Philippines has some explaining to do. These twin constructs have rarely been separated since their conjoining, in the U.S.-Philippine context, in 1898, and they have figured centrally in nearly every academic work on the subject for the last quarter century. The jaded reader may be forgiven for wondering what more can profitably be said about them. Paul Kramer does indeed explain, at great length. He presents what he calls a “transnational history of race and empire in Philippine-American colonial encounters of the early twentieth century.” So far, so straightforward. Things get a bit more complex when he elaborates: “It is, on the one hand, a history of the racial politics of empire, of the way in which hierarchies of difference were generated and mobilized in order to legitimate and to organize invasion, conquest, and colonial administration. … It is, on the other hand, a history of the imperial politics of race, of the way that empire-building interacted with, and transformed, the process of racial formation” (pp. 2–3).
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.034 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".