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2015· book-chapter· en· W4247676299 on OpenAlexaboutno aff

Bibliographic record

VenueInternational migration outlook · 2015
Typebook-chapter
Languageen
FieldSocial Sciences
TopicRegional Socio-Economic Development Trends
Canadian institutionsnot available
Fundersnot available
KeywordsInflowEmigrationOutflowGeographyImmigrationChinaQuarter (Canadian coin)Russian federationPolitical science

Abstract

fetched live from OpenAlex

The net migration inflow to the Russian Federation stood at 270 000 persons in 2014, according to official Rosstat statistics. While net immigration was lower than in 2013 (when it stood at 296 000), the gross migration inflow and outflow were both higher in 2014 than in 2013. The migration inflow in 2014 reached 578 000, an increase of 20% over the 2013 level. Immigrants in 2014 mainly came from other CIS countries: Uzbekistan (131 000 persons), followed by Ukraine, Kazakhstan and Tajikistan. Among non-CIS countries, People’s Republic of China was the main origin of immigrants (11 000 persons). As in 2013, immigrants from Uzbekistan accounted for one-quarter of the entire inflow. At 308 000 persons, the migration outflow was particularly high in 2014, likely because foreign workers whose registration expires are counted as emigrants. The outflow was mainly directed to the CIS countries of Uzbekistan (94 000), Tajikistan (35 000) and Ukraine (30 000), and to China (9 000). The highest net inflow from any country came from Ukraine (80 000).

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.767
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.003

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 head0.072
GPT teacher head0.329
Teacher spread0.257 · 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
GenreOther

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
Published2015
Admission routes1
Has abstractyes

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