Hate Crime in the News: The Media’s Role in Agenda Setting
Bibliographic record
Abstract
Examining the extant literature on hate crimes shows that there has been an evolutionary process of Hate Crime legislation (HC). Similar to other social movements such as civil rights, the hate crime movement also had various waves which eventually lead to the passage of legislation. By and large, however, HC research has focused on victims and offenders of hate crimes as well as motivations of bias. Moreover, less research has been done on the media’s portrayal and coverage of HC. Since the media is a noted influencer in social issues (Culotta, 2002; Quisenberry, 2001), we sought to answer how the news media are reporting incidences of hate crimes – particularly LGBTQ+- and compare them with official crime statistics reported by law enforcement agencies. In order to answer these questions, our research utilized a qualitative content analysis using QSR NVivo 12.0 to identify potential themes and trends which may be overlooked in simple quantitative methods. Our dataset comes from the Hate Crime Index ("ProPublica," 2018), for the month of June 2018. Official FBI data is also utilized for comparison, spanning from 2012 to 2016. Our results suggest that the media reports HC within an overall internal Agenda Setting Orientation. During analysis, two main themes were identified that show the (i) media report both the failures and challenges of law enforcement in dealing with HC issue and, (ii) that media highlights various best practices some agencies engage in. Limitations and future research directions are discussed.
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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 | 0.014 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.020 | 0.020 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".