Bibliographic record
Abstract
Criminological Aspects of Hate Crime Abstract The purpose of this thesis on the topic of the Criminological Aspects of Hate Crime is to present a concise summary of essential and available information on the issue of these crimes and to highlight specifics of these criminal activities from other types of crime. The thesis is divided into ten chapters according to the selected thematic areas. The first chapter is focused on concept of hate crime and its characteristic. This chapter offers various conceptions of hate crime, its definitions, brief history, development and description of spreading of this concept. A common feature of the definitions of hate crime mentioned in this thesis is that hate crime is a crime committed by the offender due to some strongly negative emotion, which this offender has towards variously defined groups of the population. The second chapter describes the forms of hate crimes in terms of the nature of the offender's conduct and in terms of the offender's motivational background. The third chapter provides an overview of the legal regulation of hate crime in the Czech Republic and in the selected countries (USA, Canada and FRG). The end of this chapter contains a comparison of the legislation of the Czech Republic with the legislation of the selected countries. The fourth chapter...
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".