Bibliographic record
Abstract
In the Filipina/o community that anthropologist Roderick N. Labrador encountered upon his arrival in Hawai‘i in the early 1990s, new immigrants from the Philippines clashed with fourth-generation descendants of plantation workers and Asian Americans joked about Filipino dog eating. These complexities led Labrador to ask, “What does it mean to be Filipino in Hawai‘i?” His engaging new book, Building Filipino Hawai‘i , is an outstanding addition to a growing field of studies focused on Filipina/o American community building and identity formation. Using interviews and rich ethnographic research, Labrador explores Filipina/o community in Hawai‘i through examining the relationships between power, race, culture and ethnicity, identity, class, language, and issues of the legacies of American colonialism and Asian American settler colonialism, immigrant rights, home and homeland, globalization, new mass medias, and transnationalism. Labrador rightly insists that any understanding of the Filipina/o presence in Hawai‘i must account for two colonizations: U.S. colonialism in the Philippines and Hawai‘i. As Filipinos in Hawai‘i dominate in the lowest status work (as hotel cleaners and janitors), with the least political power, Labrador’s book offers a sharp challenge to popular understandings of Hawai‘i as a model of multiculturalism and racial harmony and of the problematic panethnic identity of “local” (a nonwhite person born and raised in Hawai‘i), which, as Labrador writes, flattens out highly unequal access to wealth and power amongst groups and forms the ideological basis for contemporary settler colonialism.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.040 | 0.011 |
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".