MétaCan
Menu
Back to cohort
Record W3212834593 · doi:10.1093/sf/soab125

Legal Cynicism and System Avoidance: Roma Marginality in Central and Eastern Europe

2021· article· en· W3212834593 on OpenAlexafffund
Ioana Sendroiu, Ron Levi, John Hagan

Bibliographic record

VenueSocial Forces · 2021
Typearticle
Languageen
FieldHealth Professions
TopicRomani and Gypsy Studies
Canadian institutionsUniversity of Toronto
FundersWeatherhead Center for International Affairs, Harvard UniversityUniversity of TorontoHarvard University
KeywordsCynicismDisadvantageInequalityFace (sociological concept)SociologyPolitical scienceSocial psychologyCriminologyPsychologyLawSocial sciencePolitics

Abstract

fetched live from OpenAlex

Abstract The Roma are Europe’s largest minority group and face extensive discrimination across the continent. Drawing on a survey of Roma and non-Roma households in twelve Central and Eastern European countries, we analyze the extent to which legal cynicism, as a cognitive frame, is connected to the avoidance of helpful social institutions. We thus expand existing research on legal cynicism to focus on individuals’ contacts with potentially helpful institutions that can buffer inequality. We conclude that the interplay of legal cynicism and system avoidance, which have provided deep insights into the reproduction of structural disadvantage in American cities, also provide us with international insights into the causes of inequality and minority disadvantage across hundreds of towns in Central and Eastern Europe. In this way, legal cynicism and system avoidance work to reproduce durable inequality.

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)0.002
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.004
Scholarly communication0.0020.001
Open science0.0000.003
Research integrity0.0010.001
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 head0.039
GPT teacher head0.367
Teacher spread0.328 · 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 designQualitative
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

Citations5
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueSocial ForcesSame topicRomani and Gypsy StudiesFrench-language works237,207