Bibliographic record
Abstract
This dissertation explores practices of voice encompassing speech, song, chant, and silence among Cambodian Americans of the city of Lowell, Massachusetts, home to the second largest Khmer population outside of Cambodia. Situated around samleng, the Khmer word for voice, as a medium for Cambodian American space-claiming and self-making, this dissertation asks, how might voices from Cambodian communities of Lowell assemble diverse experiences of Cambodian refugeehood? I address this question through conceptions of vocality emergent from the stories and reflections of my collaborators, and my research weaves a collage of case studies that exemplify how voices map diverse experiences of Cambodian un- and resettlements. Running through this collage is the notion of voice as a social, cultural, individual, and political resource in experiences of Cambodian American refugeehood, which now spans fifty years and multiple generations. Cambodian Americans use voice––broadly defined and practiced––to craft senses of identity, claim space, and build community. Listening to voices of Cambodian diaspora as both material and metaphorical records of refugeehood extends to the present and offers insight and intervention into the current political climate around refugeehood in the US and the world writ large as climate change, depleted economies, and war displace millions of people at increasing rates.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".