Bibliographic record
Abstract
Between the time of first arrival of the Hmong refugees in 1975 and the mid-1990s, there has been much geographic movement of these new Americans.An initial pattern of Hmong residential dispersal throughout the American states has gradually transformed into a predominantly tri-state concentration (California, Wisconsin, and Minnesota).This highly distinctive resettlement pattern is the result of delicately balancing the most essential substance of Hmong tradition with pragmatic considerations such as job prospects (especially farming work), access to language and job training programs, extended family and clan obligations, changing federal policies for Refugee Cash Assistance (RCA), changing welfare eligibility regulations between the states (especially as it relates to AFDC-UP), climate and topographical considerations, and the like.This paper details how the Hmong settlement profile within the United States has shifted between 1983 and the mid-1990s as a consequence of secondary migration.Quantitative comparison among Vietnamese, Cambodian, Laotian, and Thai settlement patterns throughout the United States is provided.The remainder of the paper attempts to explain why it is that 89 percent of all the Hmong in the United States currently reside in only three states.The broad conclusion reached is that the primary factors driving this dynamic pattern of Hmong resettlement are "economic betterment initiatives" and "extended family and clan obligations".The other factors cited above appear to have more derivative or secondary importance as influences upon Hmong resettlement.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.024 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 0.006 |
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".