Beyond the “Haves and Have Nots”: Using an Interdisciplinary Approach to Inform Federal Data Collection Efforts with Indigenous Populations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study demonstrates how multiple methods can inform national survey data collection efforts for Indigenous populations using Pacific Islanders as a case study. National data surveys are oftentimes limited in how they collect data on small populations due to data suppression, and they lack nuance in how they aggregate distinct populations. I conduct linear regression models of U.S. Census data to demonstrate that Pacific Islanders lag behind Whites in income, even after controlling for household characteristics and geography. Further analyses of oral histories and interviews with Pacific Islanders demonstrate that income disparities exist in part because of remittances, competing financial demands, and citizenship status. I argue that it is important to add survey questions that capture migrant experiences to improve national data survey collection efforts. By utilizing and improving both types of data collection, researchers can better comprehend the barriers and opportunities for decreasing the racial income and wealth gap, which will strengthen the economic stability of Pacific Islanders in the United States.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.017 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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 it