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Record W1482274205

The Healthy Immigrant Effect and Immigrant Selection: Evidence from Four Countries

2006· preprint· en· W1482274205 on OpenAlex
Sidney H. Kennedy, James Ted McDonald, Nicholas Biddle

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueRePEc: Research Papers in Economics · 2006
Typepreprint
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsImmigrationUnobservablePositive selectionDemographic economicsCountry of originSelection (genetic algorithm)PhenomenonPolitical scienceEconomicsBiology
DOInot available

Abstract

fetched live from OpenAlex

The existence of a healthy immigrant effect – where immigrants are on average healthier than the native-born – is now a well accepted phenomenon. There are many competing explanations for this phenomenon including health screening by recipient countries, healthy behaviour prior to migration followed by the steady adoption of new country (less) healthy behaviours, and immigrant self-selection where healthier and wealthier people tend to be migrants. We explore the last two of these explanations for the healthy immigrant effect by examining the health outcomes, health behaviours, and socio-economic characteristics of immigrants from a range of source countries in the US, Canada, UK and Australia. We find evidence of strong positive selection effects for immigrants from all regions of origin in terms of education. However, we also find evidence that self-selection in terms of unobservable factors is an important determinant of the better health of recent immigrants.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.510
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.034
GPT teacher head0.361
Teacher spread0.327 · 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