Die Zuwanderung unter Arbeitsmarktgesichtspunkten in Australien, Kanada, USA, Schweiz (Immigration in Australia, Canada, the United States and Switzerland considering job market aspects)
Bibliographic record
Abstract
"The immigration of workers will remain a topic in future. That is why it makes sense to take a look at the immigration regulations of countries with high immigration figures. Switzerland was selected in addition to the classical immigration countries of Australia, Canada and the USA. The proportions of foreign workers in these countries range from 12 per cent (USA) to 25 per cent (Australia). Germany in comparison: 9 per cent. In all four countries labour-market-related immigration plays an important role, with a certain exception for the United States, where family reunification is the biggest category. Temporary and permanent residence co-exist in all of the countries, with possible transitions in each case. The three traditional immigration countries permit permanent immigration from the very beginning. Switzerland - similarly to Germany - at first allows only a temporary immigration and the residence status becomes more established over time. The setting of quotas remains primarily a political decision embedded in a consultation process. There is no labour market model on the basis of which the exact number of immigrants and their structure can be laid down. While Switzerland and the United States have not introduced a points system as a control mechanism, Canada and Australia admit permanent immigrants into their country only after they have achieved a minimum number of points. When weighting, great value is attached to considerations related to the labour market and integration such as skills and language knowledge. In the comparison it also becomes obvious that the countries of origin have become more and more diversified - e.g. in Canada the biggest groups of immigrants come from China and India. Furthermore, it has to be noted that when discussing immigration not only immigration but also out-migration has to be taken into account. The latter can reach considerable orders of magnitude. If the necessity of immigration is approved, then immigration policy ultimately has to answer four questions: Who should come? How many? Where from? For how long? Here the experiences of migration research should not be ignored: (1) It is impossible to set a magic number for immigration. (2) Immigration policy has to be clearly regulated on the one hand, and be able to react flexibly on the other hand. (3) When making long-term decisions, the approval of the population is necessary. (4) For a foreign worker initially admitted temporarily, there will be ways and means for him/her to stay longer or permanently (there is nothing more permanent than temporary foreign workers)." (Author's abstract, IAB-Doku) ((en))
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".