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Record W4392294111 · doi:10.1177/02697580241232436

Towards a multifactorial framework of the Roma’s victimisation: Discrimination, risky situations, and acceptance of violence as correlates of physical assault and harassment

2024· article· en· W4392294111 on OpenAlexaff
Lorena Molnar, Julien Chopin, Yuji Z. Hashimoto, Alexander T. Vazsonyi

Bibliographic record

VenueInternational Review of Victimology · 2024
Typearticle
Languageen
FieldHealth Professions
TopicRomani and Gypsy Studies
Canadian institutionsUniversité LavalSimon Fraser University
Fundersnot available
KeywordsVictimisationHarassmentCriminologyPoison controlSuicide preventionEthnic groupHuman factors and ergonomicsPsychologyInjury preventionPopulationSocial psychologyPolitical scienceMedicineEnvironmental healthLaw

Abstract

fetched live from OpenAlex

The European Roma population faces violence and discrimination, but the causes of their victimisation are not well understood. This study used a multi-theoretical framework to analyse data from a representative sample of 2,913 Roma surveyed in European Union Minorities and Discrimination Survey II. The results showed that police stops perceived as ethnically motivated, exposure to risky situations, and acceptance of violence when insulted predicted physical victimisation and harassment. To reduce victimisation, recommendations include sensitising police officers, diverse police patrols, crime-reduction measures in neighbourhoods, and education on nonviolent communication. Further research is needed to understand other forms of victimisation among the Roma. The study highlights the usefulness of testing multiple risk factors from different criminological theories to address victimisation of the Roma ethnic minority.

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)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.565
Threshold uncertainty score0.289

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
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)0.0000.000

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 head0.027
GPT teacher head0.430
Teacher spread0.403 · 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 teacher head, 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

Citations2
Published2024
Admission routes1
Has abstractyes

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