Legislating Against Hate Crime: Considering International Frameworks for an Irish Context
Bibliographic record
Abstract
Across the common law world, legislation which has the aim of combating hate crime has become a familiar part of the criminal code. The Irish State has been historically reluctant to introduce hate crime legislation, and this thesis examines the need for, and the potential form of, hate crime legislation in an Irish context. The purpose of this research is to provide an account of how hate crime is currently addressed through the Irish criminal justice process, and to explore options for legislative reform in this context. Thus, this thesis first seeks to understand why we punish ?hate?, and place the legislative experience internationally in its historical context by looking to the early hate crime provisions as introduced in the United States. It then describes and explores how hate crime is currently addressed in an Irish context through a comprehensive analysis of case law. It sets out Ireland?s obligations at an international level, as well as examining how Ireland complies with international standards in the area. The thesis examines the international approach to legislating for hate crime in a comparative perspective by analysing legislation from Canada, England and Wales, and Northern Ireland. \n\nOverall, through a doctrinal and comparative analysis, the thesis identifies key considerations which should be taken into account when legislating for hate crime in Ireland. It also incorporates a socio-legal dimension which, for example, includes the views of criminal justice practitioners in determining both the need for, and form of, legislation. It seeks to balance the rights of the victims of hate crime with the rights of the alleged offender in this regard, asking when it is legislatively appropriate to use the label of, for example, criminal racist, or criminal homophobe. It considers the particular context of the Irish criminal justice process, both from a practical and constitutional perspective, and ultimately makes informed proposals for legislation based upon lessons drawn from the experiences of other jurisdictions, which are fit for purpose in an Irish context.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".