Maahanmuutto- ja innovaatiopolitiikat kansainvälisessä osaajakilpailussa : Tarkastelussa Suomi, Irlanti, Iso-Britannia, Kanada, Saksa ja Yhdysvallat
Bibliographic record
Abstract
The object of the study was to chart the rules, regulations and practices steering the international mobility of researchers and highly skilled labour. The study examined the science and innovation policy measures as well as immigration policy measures taken in fi ve countries – Ireland, Great Britain, Canada, Germany and the United States – for the purpose of attracting particularly highly skilled employees working in the fi eld of research and development. In an attempt to respond to the more general needs for labour and to compensate for the lack of expertise in specifi c fi elds, the countries have implemented active work-based immigration policy. The main aim of the study was thus to bring to the fore the practices that can be exploited when the possibilities of Finland to attract expert workforce from the international labour market into the country are investigated. Systematic country-specifi c reviews are based on the situation in the period 2005–2006, after which changes were observed mainly when they concerned Finland. The most important means to promote immigration are reforms made in immigration practices, which have made immigration easier and faster for highly skilled experts. Different countries make use of different programmes for highly skilled and different point systems. Particularly the public R&D sector employees are also encouraged to immigrate by the implementation of various measures within science and innovation policy, for example, by creating specifi c grant-awarding programmes and by opening up the national research system. The study also charted the measures taken by Finland regarding its current immigration policy and science and innovation policy that affect the country’s capability of attracting foreign highly skilled professionals and experts. On the basis of countryspecifi c reviews, proposals are made to improve Finland’s capability of attracting highly skilled workforce. The main proposals are: In organizing work-based immigration, the role of working life and business life as well as expertise should be taken into account better than before. In Finland, the high-quality education and research system and its internationalization should be supported and particularly strong areas of expertise should be invested in. On a general level, this will attract also foreign highly skilled labour. -
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.009 |
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; both teacher heads agree on what is shown here.
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".