Opportunities for Community - University Partnerships: Implementing a Service-Learning Research Model in Asian American Studies
Bibliographic record
Abstract
Over the last quarter century, many Asian American Studies (AAS) programs have gradually gained academic legitimacy within universities as part of the movement for Ethnic Studies. The pressures of fighting for legitimacy in a system where research, not community-based work, is rewarded mean that the growing institutionalization of AAS has made the majority of programs and courses less accessible to communities. This article calls for AAS to take a more active, practical, and broader approach in reaching out to Asian Pacific Americans (APA) in our community, especially the underserved who face several obstacles in achieving their goals due to lack of access, lack of education, and discrimination. Asian American Studies now devotes a smaller share of its growing resources to community-orientated and community-based courses than at its inception, exacerbating the divide between the university and APA communities. Asian American Studies must return to its roots as a social agent in a broader social movement for equality and justice. This article introduces a service-learning research model that is one approach to linking the Asian Pacific American community with university Asian American Studies departments and programs across the nation.
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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 | 0.069 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.023 | 0.035 |
| Scholarly communication | 0.020 | 0.023 |
| Open science | 0.005 | 0.030 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".