Comparing Media and Law Enforcement Reports on Anti-Asian Hate Incidents During the COVID-19 Pandemic: Data Visualization Approach
Bibliographic record
Abstract
BACKGROUND: During the COVID-19 pandemic, anti-Asian hate incidents (AAHIs) increased conspicuously. Literature reports discrepancies in how crimes are reported differently in media and law enforcement data, emphasizing potential biases and inconsistencies in AAHI reporting. Understanding the discrepancies in AAHI reporting between the two sources is crucial for improving documentation procedures and addressing systemic issues in reporting mechanisms. OBJECTIVE: This study aimed to (1) present the monthly trends in AAHI counts reported by media and law enforcement data from 2020 to 2021, (2) investigate variations in AAHI counts across states and counties for each year, (3) examine discrepancies in AAHI reporting between the two sources at state and county levels during the same period, and (4) delineate differences in the types and geographic distribution of incidents as represented by the two sources. METHODS: This study used two data sources for AAHIs, media data (n=1288) from The Asian American Foundation and law enforcement data (n=1086) from the Federal Bureau of Investigation, for the 2020-2021 period. Descriptive analyses were conducted to evaluate monthly trends, state and county-level variations, and differences in incident types and locations. Ratios of reported incidents between the two sources were calculated to assess discrepancies. Temporal trends were contextualized within key sociopolitical events to offer insights into reporting dynamics. RESULTS: First, both media and law enforcement data presented a sharp increase in reported AAHIs following the first confirmed COVID-19 case in the United States, peaking around March 2020, coinciding with controversial political rhetoric. A second peak occurred from March to April 2021, immediately following the pandemic's peak, and was followed by a decline as the situation improved. Second, in the two data sources, the state-level analysis indicated that California, Texas, New York, and Washington consistently reported the highest AAHI counts. In 2021, there were notable increases in reported incidents in states such as Wisconsin and Illinois. County-level data revealed persistent high counts in California, particularly in Los Angeles County. Ratios of AAHI counts between the two data sources presented significant discrepancies, with higher ratios in California and New York. Finally, the analysis of incident types revealed that media data reported a higher proportion of harassment (477/1288, 37%), while the law enforcement data reported more property-related incidents (239/1086, 22%). Regarding location types, media data frequently reported incidents in public areas (515/1288, 40%) and businesses (361/1288, 28%), whereas law enforcement data reported more incidents occurring in residential settings (201/1086, 18.5%). CONCLUSIONS: This study highlighted significant trends and disparities in AAHI reporting between media and law enforcement data, underscoring the need for a nuanced understanding of how these incidents were reported. Practice and policy implications suggested fostering community engagement to support Asian communities while enhancing the accuracy and consistency of hate crime reporting.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.022 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".