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Record W2803235928 · doi:10.1017/dem.2017.19

DECOMPOSING GAPS BETWEEN ROMA AND NON-ROMA IN ROMANIA

2018· article· en· W2803235928 on OpenAlexaff
Christopher Rauh

Bibliographic record

VenueJournal of Demographic Economics · 2018
Typearticle
Languageen
FieldHealth Professions
TopicRomani and Gypsy Studies
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsEthnic groupUnemploymentDemographic economicsEducational attainmentLiteracyFace (sociological concept)DemographyPsychologyPolitical scienceSociologyEconomic growthEconomicsSocial science

Abstract

fetched live from OpenAlex

Abstract It is widely known that the Roma have been suffering persistent disadvantages. Yet, little empirical evidence exists. Using the censuses of 1977, 1992, 2002, and 2011, I provide a comprehensive overview of the past, present, and an outlook on the future of the Roma in Romania, home to a large and rapidly growing Roma community. Young Roma, in particular girls, are less likely to be attending school, indicating that lack of educational attainment is likely to persist. The Roma have worse housing conditions and face lower employment and higher unemployment levels. Amongst Roma, females are less likely to be employed than males. Oaxaca–Blinder decompositions of the ethnic and gender employment gaps reveal that the differences in employment cannot be fully explained by observables, such as age or education. Despite the seemingly dire picture, there are signs of improvement for more recent cohorts, as literacy rates have reached close to universal levels.

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)0.004
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 score0.026
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.034
GPT teacher head0.363
Teacher spread0.329 · 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

Citations18
Published2018
Admission routes1
Has abstractyes

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