Rage for Order: The British Empire and the Origins of International Law 1800–1850 by Lauren Benton & Lisa Ford
Bibliographic record
Abstract
Fittingly, Rage For Order by Lauren Benton, Professor of Law and History at Vanderbilt University, and Lisa Ford, Associate Professor of History at the University of New South Wales, takes both its title and its epigraph from the last stanza of Wallace Stevens’ “The Idea of Order at Key West.” Originally written on the then-sparsely inhabited island of Key West in Florida, the poem blends the sound of a woman singing with the ocean and uses that voice to delineate the various boundaries between wave, sky, and horizon line. Beginning with the stanza quoted above, Stevens ends up identifying the ocean with that voice, and describes the singer as the one who creates that ocean with her singing, before he then describes our “rage” for order. Put another way, Stevens writes a poem about how man—or woman!—is the one who orders the natural world, delineating with light and sound the various zones and poles of the ocean. It is a fitting epigraph precisely because the project of empire and international law both are similar attempts by humankind to order the world. For this book, however, the key word in the poem is, of course, rage: rage as a thoughtless, uncontrolled passion; rage as a deeply damaging action; rage as a fashionable craze. All three understandings of rage have their place in the narrative of empire spun by Benton and Ford.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".