Estimating the Refugee Population from PUMS Data: Issues and Demographic Implications
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
Discussions of immigration, settlement and adjustment within the U.S. do not typically refer to immigrant status (i.e., refugee versus family preference), and instead refer to the foreign–born population as an aggregate. Distinguishing between refugees and other immigrant arrivals likely means differences with respect to their geographic distribution and embodied human capital owing to differences associated with the reasons for immigration (forced versus voluntary), period of arrival, and immigration policy. The lack of differentiation by group within the existing literature is typically due to a shortfall of detailed information relating to admission status within publicly released data files. Yet concrete knowledge of differences by admission category is important in understanding overall patterns of settlement and adjustment within the foreign–born population. This paper therefore explores potential differences with respect to settlement and endowed human capital between immigrants and refugees. Identification of the major sources of refugees within Immigration and Naturalization Service data files allows the refugee population to be identified within the 1990 Public Use Microdata Sample (PUMS), therefore increasing the range of variables and measures associated with the refugee population available to researchers, and points to the diversity of the refugee population.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it