Race, Surveillance, and Indian Anticolonialism in the Transnational Western U.S.-Canadian Borderlands
Bibliographic record
Abstract
On December 2, 1910, the SSMinnesota sailed into Seattle harbor carrying nineteen Asian Indian migrants from the Philippine islands who insisted that, because they had traveled from one part of the “United States” to another, they had a right to be admitted to the U.S. mainland. Immigration inspectors, increasingly anxious that Asian migrants would use the Philippines as a back door through which to gain entry to the U.S. mainland, immediately issued deportation orders but quickly learned that prohibiting entry to Indians from the Philippines would not be as simple as they expected. These migrants had not come from a foreign port, but from a U.S. territory where they had gained legal entry, and theirs were the first of a series of immigration challenges over the next three years in which Indian migrants sought to circumvent discriminatory immigration policies and practices at U.S. mainland ports by taking alternate routes across the American empire. Indian leaders used these immigration cases to highlight the contradictions of empire and to call for the overthrow of British rule in India. Meanwhile, U.S., Canadian, and British officials linked the political mobilization of Indians around these cases to earlier warnings that the Pacific Coast was becoming a center of sedition where Indians were challenging and exploiting restrictive immigration policies to advance radical agendas.1
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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 | 0.004 |
| Science and technology studies | 0.039 | 0.021 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".