Legal mechanisms of economic and socio-cultural adaptation of migrants in foreign federative states: problems and solutions✳
Bibliographic record
Abstract
The article examines the main trends in the development of immigration legislation in the federal states, focusing on the cur- rent state of the legal regulation of the adaptation and integration of migrants and on the basis of the relevant Canadian experience offers prospective directions of its use in the Russian Federation. The issues of the division of powers between the various levels of public authority in the field of adaptation and integration of foreign nationals in a federal state. The issues of the division of powers between the various levels of public authority in the field of adaptation and integration of foreign nationals in a federal state. The problems of legal regulation of the processes of adaptation and integration of foreign nationals, the ratio of legal mechanisms used to attract quali- fied foreign specialists. The priority of the legislative regulation of immigration policy in the Russian Federation, the author sees the need for a clearer division of powers between the various levels of public authority in the field of adaptation and integration of migrants, taking into account the specifics of economic development, historical, demographic, and other local conditions of each of the Russian Federation for the most effective engagement and use in their territory external migration resources.
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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".