Beyond the “Haves and Have Nots”: Using an Interdisciplinary Approach to Inform Federal Data Collection Efforts with Indigenous Populations
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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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 | 0.214 | 0.207 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.024 | 0.012 |
| Scholarly communication | 0.012 | 0.020 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".