Effects of Selection Criteria and Economic Opportunities on the Characteristics of Immigrants
Bibliographic record
Abstract
International migration is a joint outcome of the individual's desire to migrate and the host country's selection process. First, the potential migrants apply to a host country, then the host country chooses migrants from the applicant pool. The theoretical focus of the earlier literature was centred on the desire to migrate, while the empirical literature focused on the actual migrants, while migration is the product of these two factors. The objective of this paper is to identify the components of this two-step, decision-making process Parameters in the migration model relate directly to policy instruments such as the points awarded for various characteristics. Given the parameter estimates of the model and the general analysis of immigration policy, a study of the factors determining the individual's decision to apply can be done in a way that has not been possible up until now. Using samples of migrants and non-migrants, the model is estimated for migration from two different source countries, the United States and the United Kingdom, to Canada. For migrants, a newly available longitudinal data set, the Longitudinal Immigration Database (IMDB), has been used. The richness of this database, which surveys immigrants to Canada over a long period and contains information on both their application and subsequent earnings, permits the investigation of a large range of questions that could not be fruitfully addressed before. Estimation of the two-step framework provides important insights on the effects of factors, such as education and income, that help establish this selection process.
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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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".