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Eastern <scp>E</scp> uropean Immigrants

2016· other· en· W4232394618 on OpenAlexaboutno aff
Marzanna Farnická, Hanna Liberska, Iwona Grzegorzewska

Bibliographic record

VenueEncyclopedia of Family Studies · 2016
Typeother
Languageen
FieldSocial Sciences
TopicEuropean Politics and Security
Canadian institutionsnot available
Fundersnot available
KeywordsEmigrationImmigrationEuropean unionPoliticsLiberalizationDemographic economicsPolitical scienceDevelopment economicsGeographyEconomicsInternational trade

Abstract

fetched live from OpenAlex

The political changes in eastern Europe that took place after 1989 led to a greater number of people leaving their home countries to look for better working conditions in other countries. It is possible to identify two basic patterns of this migration: the first and most common targets are the wealthier countries of the European Union, and the second are the classical targets of emigration, such as the United States or Canada. In the majority of cases, labor migration has replaced political migration. In the European Union there is the obvious issue of economic liberalization between the member states as they attempt to integrate economically. This trend is a key factor in the facilitation of migration. Most immigrants from eastern European countries are young people aged 35 and above on average or well‐educated professionals. With increased migrational movement in these countries, new social phenomena have emerged, such as Euro‐orphanhood, fluid populations, and an increased number of mixed marriages.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.085
Threshold uncertainty score0.283

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0850.010

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 head0.039
GPT teacher head0.324
Teacher spread0.285 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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