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Record W2093592019 · doi:10.1007/s13524-010-0008-x

Disability Among Native-born and Foreign-born Blacks in the United States

2011· article· en· W2093592019 on OpenAlexaboutno aff
Irma T. Elo, Neil K. Mehta, Cheng Huang

Bibliographic record

VenueDemography · 2011
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsnot available
FundersNational Institute on AgingNational Institute of Child Health and Human DevelopmentEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentU.S. Public Health ServiceUniversity of MichiganUniversity of Pennsylvania
KeywordsForeign bornImmigrationNative-BornDemographyEthnic groupCensusPublic healthGeographyMedicinePopulationGerontologyPolitical scienceSociology

Abstract

fetched live from OpenAlex

Using the 5% Public Use Micro Data Sample (PUMS) from the 2000 U.S. census, we examine differences in disability among eight black subgroups distinguished by place of birth and Hispanic ethnicity. We found that all foreign-born subgroups reported lower levels of physical activity limitations and personal care limitations than native-born blacks. Immigrants from Africa reported lowest levels of disability, followed by non-Hispanic immigrants from the Caribbean. Sociodemographic characteristics and timing of immigration explained the differences between these two groups. The foreign-born health advantage was most evident among the least-educated except among immigrants from Europe/Canada, who also reported the highest levels of disability among the foreign-born. Hispanic identification was associated with poorer health among both native-born and foreign-born blacks.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.033
GPT teacher head0.303
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations69
Published2011
Admission routes1
Has abstractyes

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