“I Am Already Annexed”: Ramon Reyes Lala and the Crafting of “Philippine” Advocacy for American Empire
Bibliographic record
Abstract
Abstract This article reconstructs the American career of the Manila-born author Ramon Reyes Lala. Lala became a naturalized United States citizen shortly before the War of 1898 garnered public interest in the history and geography of the Philippines. He capitalized on this interest by fashioning himself into an Oxford-educated nationalist exiled in the United States for his anti-Spanish activism, all the while hiding a South Asian background. Lala's spirited defense of American annexation and war earned him the political patronage of the Republican Party. Yet though Lala offered himself as a ‘model’ Philippine-American citizen, his patrons offered Lala as evidence of U.S. benevolence and Philippine civilization potential shorn of citizenship. His embodied contradictions, then, extended to his position as a producer of colonial knowledge, a racialized commodity, and a representative Filipino in the United States when many in the archipelago would not recognize him as such. Lala's advocacy for American Empire, I contend, reflected an understanding of nationality born of diasporic merchant communities, while his precarious success in the middle-class economy of print and public speaking depended on his deft maneuvering between modalities of power hardening in terms of race. His career speaks more broadly to the entwined and contradictory processes of commerce, race formation, and colonial knowledge production.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.013 | 0.014 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".