MétaCan
Menu
Back to cohort
Record W2550844154 · doi:10.26417/ejser.v1i1.p65-69

Tendencies of High-Skilled Migration coming from Romania. Favourable Legislation and Social Policies

2014· article· en· W2550844154 on OpenAlexaboutno aff
Alexandra Vîlcu

Bibliographic record

VenueEuropean Journal of Social Sciences Education and Research · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicRegional Development and Policy
Canadian institutionsnot available
FundersAcademia RomânaEuropean Commission
KeywordsEmigrationPopulationPhenomenonLegislationGlobalizationImmigrationPolitical scienceEuropean unionEconomic growthDevelopment economicsDemographic economicsSociologyEconomicsLawEconomic policy

Abstract

fetched live from OpenAlex

The external migration of a significant part of Romania's high-skilled population is a social phenomenon which became increasingly frequent starting from the 1990s, right after the fall of the communist regime. The basis for this phenomenon consists of several causes: globalization, the strengthening of international economic relations, and later on, Romania's adhesion to the European Union. Research has shown that of all high-skilled population, the professionals who emigrate more frequently consist of engineers, teachers, medical staff, scientific researchers, economists and architects. Besides, the chosen destinations have been variable throughout time. The first phase in time took place in the 1990s, when a large part of the high-skilled population chose to emigrate for professional purposes in countries such as The United States of America, Canada, Germany or Israel. The second important phase occurred after year 2000, when the focus was placed on EU countries, especially after Romania's integration. Apart from temporary unqualified migration, the number of high-skilled migrants and those who leave the country to continue their studies also soared. The chosen countries generally include Great Britain, Germany, Belgium, France and Austria. Given these differences in the tendencies of high-skilled migration, this paper will offer an insight on how the phenomenon evolved, and the factors that caused these variations in space and time. Most likely, some of the countries that were preferred have been facilitating the integration of high-skilled immigrants in society, as opposed to unqualified ones, through a selective set of laws and social policies which are meant to favour this social category. Therefore, we will discover and analyze various examples and benefits of legislation and social policies which offered social protection to high-skilledimmigrants in various countries. This paper is made and published under the aegis of the Research Institute for Quality of Life, Romanian Academy, as part of the programme co-funded by the European Union within the Operational Sectorial Programme for Human Resources Development, through the Project for Pluri and Interdisciplinarity in doctoral and post-doctoral programmes. Project code: POSDRU/159/1.5/S/141086

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.686
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.087
GPT teacher head0.407
Teacher spread0.321 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueEuropean Journal of Social Sciences Education and ResearchSame topicRegional Development and PolicyFrench-language works237,207