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
Highly Skilled Labour Migration in Europeicies in the EU with those in place in selected non-EU countries.In addition to attracting new workers, recent international student graduates also constitute a potential pool of highly skilled workers.Designing policies that allow graduates to transition from their studies into the host-countries' labour market offers another way of building and strengthening a country's highly skilled labour pool.Such policies will be investigated in the last section. MIGRATION INTO THE EUROPEAN UNIONIn 2015, the EU-28 member states experienced a total inflow of 4.7 million migrants (Eurostat 2017a) with 2.4 million migrants coming from non-EU member countries.Figure 1 shows residence permits issued by authorities of EU member states to third country nationals between 2008 and 2016 (in thousands), categorised by four main reasons for migration, namely employment, education, family reunification and 'other reason', which includes humanitarian reasons. 2 In 2016 there was a sharp increase of 28% in the number of residence permits issued, with 733,484 more permits issued than in 2015.The upturn was mainly due to 'other reasons,' which increased by 400,509 permits, with 280,000 permits issued to beneficiaries of international protection.With over one million permits distributed for 'other reasons', this category accounts for 31% of all permits in 2016.The second largest share of permits was employment-related with 852,747 (25%), followed by 779,301 family-related permits (23%) and 694,648 education related ones (21%).Throughout the past years the share of labour migration has been relatively constant at approximately 25%.However, while the overall size of migration flows is relatively large in Europe, 25% is a relatively small share of economic migrants in comparison to traditional destination countries.In Canada, the share of economic migrants reached over 60% throughout the past five years (CIC News 2017).Looking closer at individual countries within the European Union, the United Kingdom issued 865,894 permits in 2016, followed by 585,969 permits granted in Poland and 504,849 in Germany.A detailed overview is provided in Table 1.In terms of employment-based permits, Poland issued 493,960 permits in 2016, making up 84% of its total authorisations.The United Kingdom and Germany issued relatively few employment-based permits, which accounted for just 14% 2 The category 'others' also includes stays without the right to work and people in the process of a permission authorisation.and 8% of their total authorisations respectively.Residence permits based on education represent 21% of all residence permits issued among EU-28 countries in 2016.The United Kingdom accounts for over 50% (365,455 permits), demonstrating its continuing attractiveness as a destination for education purposes.As far as family-motivated migration is concerned, Germany leads with 136,982 permits issued in 2016.In terms of the absolute number of permits issued for humanitarian and international protection reasons, the United Kingdom (294,022) and Germany (282,232) issue the highest number of permits.However, if looking at the share of these permits, Germany (56%), Sweden and Austria (both 51%) are at the top of the list with the United Kingdom (34%) in 8 th place.A closer look at the skill composition within the labour force reveals that access to skilled labour is crucial for innovation in firms, as well as for the growth and development of the economy.If firms cannot meet their demand for skilled labour, this may result in skill shortages, which is defined as a state in which an employer and the economy lack skilled workers, to the extent that there are not enough people with a particular skill to meet demand.According to a study by Man Power Group (2017), Figure 2 shows the percentage of firms with 10 or more employees whose management Table 1
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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