Digitally Witnessing Police Brutality: Examining the Relationship Between Police Violence, Race, and Affect in the Age of Social Media
Bibliographic record
Abstract
Police brutality has recently become a popular topic following the summer of 2020. However, long before this time, police brutality and black trauma specifically had been explored by many others. With the emergence of social media, the exhibition of black bodies in trauma has been a sight for public gathering and debates. Previous research has shown us the inconsistencies that exist between the treatment of black and white bodies who experience violence in general. With that being said, this study aims to explore the intersections of race, police violence, and affect in the digital space. By comparatively analyzing online commentary left under two racially different cases (Philando Castile and Daniel Shaver), one will begin to understand how a victim's race influences how others affectively respond to them and their deaths. In doing this, discussions around narrative reconstruction, racial stereotypes, and the power of sound and imagery will all become relevant. witnessing a video clip of Ahmaud Arbery running for his life to hearing the sounds of George Floyd begging for his life all while four officers lay on top of him, the emotional and intellectual toll of unpacking my own research findings while simultaneously unpacking the everyday reality of more and more African Americans being beaten and/or dying at the hands of police officers greatly impacted my own understanding of this research topic. With every news update and Instagram post, it became more and more difficult to escape my research topic. Nevertheless, the latest set of cases this summer and the various protests following were not forgotten when writing this piece. Individuals such as George Floyd, Ahmaud Arbery, Breonna Taylor, Elijah McClain, Jacob Blake, amongst others have all shaped my analysis to some degree.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".