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Record W2487963161 · doi:10.1093/melus/mlw030

Refugeography in “Post-Racial” America: Bao Phi’s Activist Poetry

2016· article· en· W2487963161 on OpenAlexaff
Vinh Nguyen

Bibliographic record

VenueMELUS Multi-Ethnic Literature of the United States · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicAsian American and Pacific Histories
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsRefugeeGender studiesOppressionSolidarityPoliticsSociologyPoeticsRacismSubjectivityIdentity (music)RacializationAestheticsPoetryPolitical scienceLiteratureLawArtRace (biology)

Abstract

fetched live from OpenAlex

This paper conceptualizes Bao Phi’s neologism “refugeography” as a poetics—a mode of expression and a politics, a way of perceiving and being in the world—that critically expands the “refugee” category. Reading Phi’s collection Sông I Sing: Poems (2011), I argue that refugeography signifies the psychic landscape of refugee subjectivity, the scope of refugee subjects (agents and topics), and the physical places of refugee movement and dwelling. This “geographically” capacious concept enlarges the purview of the refugee, insisting that racialization on American shores, police brutality, Asian American identity, diasporic consciousness, and social justice activism along with imperialism, foreign war, and forced migration are “refugee” concerns. For Phi, it is by way of war and empire’s human rem(a)inders that larger issues of identity politics, resistance to oppression, panethnic solidarity, and displacement and rootedness come to the foreground and are shown to be ongoing contemporary contestations in supposedly “post-racial” times. Refugeography thus is an enabling concept, one that begins with the figure of the refugee but does not necessarily end there. It is expansive and wide-ranging in the way it explores and sketches out subjectivities, changing positionalities, and physical geographies while promoting and celebrating Asian American community and identity.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.918
Threshold uncertainty score0.651

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.015
GPT teacher head0.293
Teacher spread0.279 · 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 designQualitative
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

Citations2
Published2016
Admission routes1
Has abstractyes

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