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Record W2597804224 · doi:10.13185/kk2017.02813

White Citizenship: A Category of Identification and Route of US Immigrant Constitution

2017· article· en· W2597804224 on OpenAlexfundno aff
Ma. Socorro Q Perez

Bibliographic record

VenueKritika Kultura · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicAsian American and Pacific Histories
Canadian institutionsnot available
FundersUniwersytet WarszawskiYork University
KeywordsWhite (mutation)White privilegeGender studiesSociologyImmigrationCitizenshipRacial hierarchyColonialismConstitutionIdeologyIdeal (ethics)NormativeHistoryRacismLawPoliticsPolitical science

Abstract

fetched live from OpenAlex

The “desire to be white” observed amongst Filipino/Ilocano-Hawaiian immigrants is not a mere personal resolve nor a sole act of individual decision. It is an aspiration driven by the ideology of “white ideal,” the discourse of middle class success, and deepened/straited by the historical junctures such as the colonial and neocolonial relationships between the US and the Philippines, immigration policies, and the sugar plantation labor history in Hawaii. The control and discipline of Filipino/Ilocano-Hawaiian immigrants are installed through the iteration of normative rules involving identification categories of race, ethnicity, and class. The identification of white ideal however may get deflected in the crisscrossing and reception at the level of social praxis, as the attempt to embody a norm is never complete (Rottenberg). Such area of ambivalence may produce fissures that present critical space for the re-encodation of Ilocano-Hawaiian representation and agency. Of note is the seamless intrication between the history and the story, between texts and contexts, or conversely, between contexts and texts in selected GUMIL Hawaii short fiction. The play of “mirroring” of white ideals and the “disidentification” of the same is precisely recuperated in the study.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.585
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.017
GPT teacher head0.282
Teacher spread0.266 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2017
Admission routes1
Has abstractyes

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