Revisiting Asian American Poetics: A Review of Heterogeneity and a Case of Russell Leong
Bibliographic record
Abstract
The term Asian America was first coined in the late 1960s. Although initially closely associated with political subversiveness and establishing a collective pan-Asian ethnic identity, the name has been continuously reinventing itself to be more diverse and elastic with the changing relationship between the United States and Asian countries and the arrival of new groups of Asian immigrants. Along the way, heterogeneity has always been an undeniable mark on the rubric of Asian America. This article revisits the expanding literary field of Asian American poetry, articulating a heterogeneous and indeterminate poetics from the movement era to the new century of globalization. It takes several poems of Russell Leong as a case study and foregrounds, through the conception tool of cultural translation, his in-betweenness, and the mixture of ethnic experiences and transnational concerns.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.001 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.008 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".