Immigration and the dependency ratio of a host population
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.
Bibliographic record
Abstract
This paper examines the long-term impact of constant immigration on the dependency ratio in an SI population (a stationary population through constant immigration) in the context of the stable population model. Our analysis has three main findings. First, in SI populations, a younger population (a lower aging ratio) does not necessarily have a lower dependency ratio. An SI population has a lower dependency ratio than a closed stationary population, provided immigration is concentrated around the youngest working age in the host population. Second, under the same condition, selecting high-fertility immigrants increases the dependency ratio. Third, also under the same condition, substituting working-age immigrants with young (dependent) immigrants increases the dependency ratio. Using the United Nations model age structures of immigrants, our empirical illustration confirms these analytical results.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it