Bibliographic record
Abstract
Immigration to European Union (hereinafter referred to as the ‘EU’) as a realityand a need of refreshing its ageing population has made the EU to recently adoptsome important documents. Traditionally, European countries seem to be more closedtowards the immigration comparing to United States of America and Canada whichenabled the entry of new population even through various lotteries. However, EU didrecognize the need for import of experts from various areas. Thus the Council hasadopted the EU Blue Card Directive for highly skilled workers (Directive2009/50/EC). Still, having in mind the legal power of a EU Directive, the membercountries are given the power to adopt their immigration policies. This paper analysesthe regulations on immigration enacted by the EU and the implementation of suchregulations at the level of member countries. Although the EU does regulate theimmigration policy, it is up to the member states to deal with particular cases. In thatrespect the paper shall also address the issues of immigration which violated theEuropean Convention on Human Rights and Fundamental Freedoms by analyzing thekey judgments of the European Court for Human Rights in Strasbourg. The issue ofresidence v. citizenship as the grounds of immigration shall also be explained. Theshort overview of inter migration in the EU, is presented for the purposes ofcomparison. The paper is based on a hypothesis that immigration policies in membercountries still lack some consistency in the implementation of EU regulations, andtherefore reveal weaknesses of the EU immigration policy. Method used in this paperis normative analysis, method of induction and deduction, comparative method andcase study.
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 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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".