A probabilistic forecast of the immigrant population of Norway
Bibliographic record
Abstract
We present a probabilistic forecast for the immigrant population of Norway and their\nNorwegian-born children (“second generation”) broken down by age, sex, and three types of\ncountry background: 1. West European countries plus the United States, Canada, Australia, and\nNew Zealand; 2. East European countries that are members of the European Union; 3. other\ncountries.\nFirst, we compute a probabilistic forecast of the population of Norway by age and sex, but\nirrespective of migration background. The future development of the population is simulated\n3 000 times by stochastically varying parameters for mortality, fertility and international\nmigration to 2060. We add migrant group detail using stochastically varying random shares to\nsplit up each result from the previous step into six sub-groups with immigration background,\nand one for the non-immigrants. The probabilistic forecast is calibrated against the Medium\nVariant of Statistics Norway’s official population projection.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".