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Record W4408534243 · doi:10.1017/s1557466014026953

Race-Making and Colonial Violence in the U.S. Empire: The Philippine-American War as Race War

2014· article· en· W4408534243 on OpenAlexfundno aff
Paul A. Kramer

Bibliographic record

VenueJapan focus · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicAsian American and Pacific Histories
Canadian institutionsnot available
FundersAteneo de Manila UniversityYork UniversityYale University
KeywordsRace (biology)EmpireColonialismSpanish Civil WarGender studiesPolitical scienceHistoryCriminologyAncient historySociologyLaw

Abstract

fetched live from OpenAlex

Speaking on May 4, 1902 at the newly-opened Arlington Cemetery, in the first Memorial Day address there by a U.S. President, Theodore Roosevelt placed colonial violence at the heart of American nation-building. In a speech before an estimated thirty thousand people, brimming with “indignation in every word and every gesture,” Roosevelt inaugurated the Cemetery as a landscape of national sacrifice by justifying an ongoing colonial war in the Philippines, where brutalities by U.S. troops had led to widespread debate in the United States. He did so by casting the conflict as a race war. Upon this “small but peculiarly trying and difficult war” turned “not only the honor of the flag” but “the triumph of civilization over forces which stand for the black chaos of savagery and barbarism.” Roosevelt acknowledged and expressed regret for U.S. abuses but claimed that for every American atrocity, “a very cruel and very treacherous enemy” had committed “a hundred acts of far greater atrocity.” Furthermore, while such means had been the Filipinos’ “only method of carrying on the war,” they had been “wholly exceptional on our part.” The noble, universal ends of a war for civilization justified its often unsavory means. “The warfare that has extended the boundaries of civilization at the expense of barbarism and savagery has been for centuries one of the most potent factors in the progress of humanity,” he asserted, but “from its very nature it has always and everywhere been liable to dark abuses.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.093
Threshold uncertainty score0.185

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0190.012
Scholarly communication0.0110.004
Open science0.0010.007
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.009
GPT teacher head0.284
Teacher spread0.275 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

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