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Record W2992408851 · doi:10.17816/rjls18182

Legal mechanisms of economic and socio-cultural adaptation of migrants in foreign federative states: problems and solutions✳

2016· article· en· W2992408851 on OpenAlexaboutno aff
Alena A. Mishunina

Bibliographic record

VenueRussian Journal of Legal Studies (Moscow) · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Political and Economic Relations
Canadian institutionsnot available
Fundersnot available
KeywordsLegislationLegislatureAdaptation (eye)Political scienceImmigrationForeign nationalState (computer science)Russian federationFederal lawPublic administrationLawBusinessEconomic policy

Abstract

fetched live from OpenAlex

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.225
Threshold uncertainty score0.273

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.049
GPT teacher head0.301
Teacher spread0.252 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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
Published2016
Admission routes1
Has abstractyes

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