Immigration And The Rate Of Population Mixing: Explorations With A Stylized Model
Bibliographic record
Abstract
Immigrants can mix with the population of a receiving country in various ways. We consider demographic mixing by which we mean cross-mating, and more particularly the bearing of children where one parent is of immigrant descent and the other is not - cross-parenting as we term it. We consider a hypothetical country with an initial stable population and introduce immigration. The results of cross-parenting are taken into account by identifying three separate populations within the overall total: non-immigrant population, immigrant population (immigrants and their descendants), and mixed population. We develop a stylized model to track the three populations, with interest focusing in particular on how the proportion of mixed population changes through time as it moves toward a steady state. The model has a stable projection (Leslie) matrix that holds for all three populations and moves them forward from generation to generation as each evolves in its own way. As cross-parenting occurs the resulting progeny are transferred from the other populations to the mixed population. The pattern of cross-parenting is determined in the first instance by a matrix representing preferences among the three populations and alternative preferential patterns are experimented with, ranging from complete isolation to indifference as to cross-parenting choices. However the matrix must be modified to recognize supply constraints imposed by the sizes of the available populations and a restricted least-squares procedure is employed to effect the modification while remaining as close as possible to the original preference pattern. Alternative rates of immigration are experimented with also.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".