Bibliographic record
Abstract
The sharply increasing immigration to Western Europe in recent years has been accompanied by a growing interest in the size and composition of the future number of immigrants. This interest is based on reasons ranging from racism, xenophobia and other reasons for opposing immigration, to concerns about the integration of immigrants into society and the effects of immigration on the economy. Attempts to project the population of immigrants face a number of challenges such as ethical issues, definition of immigrants, modelling, data needs and as-sumptions about future migration flows, fertility behaviour, etc. This paper presents the choices made on some of these issues in the projection of the immigrant population of Nor-way. Projections of immigrants or other ethnic or minority populations have also been made in sev-eral other countries, including Denmark, The Netherlands, Sweden and Austria. Definitions and methodology vary and depend partly on the issues of interest but also on the data avail-ability. Countries with good administrative registers, such as the Nordic countries and The Netherlands, have focused on data that are available in the registers, such as country of origin or birth. In other countries projections have been made by ethnicity/race (USA and Canada) and religion (Austria). Statistics Norway has made projections of the immigrant population in 2005 and 2008 and will publish a new set in June 2009. The immigrant population is defined by country of birth. Persons born in Norway of parents born abroad have also been included. This paper presents the methodology, data and major projection results. We found that the most sensitive factor in the projections is the assumption about the future net immigration. We have, therefore, estimated an economic model of migration to Norway, which is presented here. Finally, we will discuss possible future extensions of the model.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".