"NO ONE CARES WHAT HAPPENS TO A GYPSY" Conceptualisation and Reasons for Underreporting of Hate Crime by the EU Citizens of Roma Descent in Malmö, Sweden
Bibliographic record
Abstract
Objectives: Romania joined to European Union (EU) in 2007. Consequently, the number of EU citizens of Roma descent increased in Sweden as the movement of freedom was granted. This study attempts to understand the conceptualisation and reason for underreporting hate crimes in Malmö, Sweden. Methods: Qualitative data collection methods of semi-structured interview and participant observation were employed. Semi-structured interviews were conducted with 15 EU citizens of Roma descent and 6 Civil Society Organization Representatives (a social worker, lawyers, a project manager and a journalist/consultant). Participant observation took place in a community centre where EU citizens of Roma descent visit in Malmö. Principal Findings: The majority of the research about EU citizens of Roma descent in Malmö must address the living and working conditions of the Roma: homelessness, deprivation of basic needs and working on the streets. Participants endure hate crimes daily. The conceptualisation of the hate crime of the Roma participants is primarily in line with Swedish Criminal Code with additions. Unless severe physical harm occurs, participants do not tend to report it as they do not have time, energy or trust in authorities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".