MétaCan
Menu
Back to cohort

Social aspects of the intra-EU mobility

2021· article· en· W3184396856 on OpenAlexfundno aff
Krasimir Koev, Ana Popova

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicEducation in Diverse Contexts
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaUniverzita Palackého v Olomouci
KeywordsSocializationSocial mobilityEu countriesDemographic economicsPolitical scienceEuropean Social SurveyEconomic growthBusinessPsychologyDevelopmental psychologyEuropean unionInternational tradeEconomics

Abstract

fetched live from OpenAlex

The paper presents a topical picture of the intra-EU mobility on the basis of officially published quantitative data. Several social aspects of this type of internal migration are discussed and analyzed, such as: risks for the health, education and socialization of the migrant children; risks for the stability of the migrant families; demographic and social consequences for the EU countries which are reported as the biggest sources of intra-EU mobility. The official statistical data are compared with the results of the authors’ study on socialization deficits for the children from so called “transnational families”, where one or both parent are labor migrants and have left their children to the care of relatives in the country of origin. The comparative results serve as a basis of conclusions about the negative social impact of the intra-EU mobility on the migrant families and especially on their children.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.046
GPT teacher head0.359
Teacher spread0.312 · 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 designObservational
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
Published2021
Admission routes1
Has abstractyes

Explore more

Same topicEducation in Diverse ContextsFrench-language works237,207