MétaCan
Menu
Back to cohort
Record W3109527413 · doi:10.31558/2519-2949.2020.3.3

Особливості формування державної імміграційної політики окремих розвинених країн

2020· article· uk· W3109527413 on OpenAlexaboutno aff
Наталія Прямухіна

Bibliographic record

VenueПолітичне життя · 2020
Typearticle
Languageuk
FieldEngineering
TopicMilitary Technology and Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials science

Abstract

fetched live from OpenAlex

The article is devoted to the issues of immigration policy, which is an important political tool for achieving social goals in the field of economy, demography, security and nation building. In today's world, developed countries have joined the competition for highly skilled migrants and quality carriers of human capital, using immigration as an important resource for economic and demographic development. The unfavorable demographic situation formed in Ukraine before the beginning of the new millennium forces us to consider migration as an important resource, the use of which can compensate for the natural decrease in population. In traditional countries of reception of immigrants, there are serious problems in addressing the integration of migrants, national security, tolerance, socio-economic stability and intercultural dialogue. The immigration policy of Great Britain, Australia, Canada is studied and it is determined that any immigration policy is not without contradictions, its mechanism evolves and develops, and the institutional framework is formed within a certain political regime and reflects the political culture, values, goals of the nation. The essential characteristics of political regulation and levels of immigration policy of developed countries are formulated. Focus on the main components of the immigration policy of the studied countries, namely, issues related to the reception of migrants; the problems of their integration and socialization allow us to say that a special place among the factors that determine the process of development and implementation of immigration policy is occupied by the political regime.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.771
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.007

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 head0.016
GPT teacher head0.192
Teacher spread0.176 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueПолітичне життяSame topicMilitary Technology and StrategiesFrench-language works237,207