Understanding how hatred persists: situating digital harassment in the long history of white supremacy
Bibliographic record
Abstract
Racism, sexism, and homophobia have long been characteristic of liberal democracy in North America. Public discussion around harassment - with a particular focus on sexual violence against women - reached its ferment in the light of charges brought against a number of high-profile public figures and with the #MeToo movement. Within the academy, radical, feminist, and anti-racist scholars have recently faced targeted harassment and threats of violence through email campaigns organized online by right-wing and white supremacist online. Many have pointed to the specificity of the digital medium as facilitating new forms of harassment online. Yet, others have shown how digital spaces might be better seen in continuity with older forms of violence, that have long targeted communities of color working to challenge racist systems of oppression. We contribute to this line of thinking by examining a case of white supremacist violence against a Black family - the Wades - who purchased a home in a predominantly white neighbourhood in Louisville, Kentucky in the 1950s. With attention to both Black and digital geographies, we juxtapose the Wade’s story with critical work on newer forms of white supremacy online. We show how hatred persists alongside shifts in medium, and question the extent to which digital platforms create new opportunities for harassment and violence.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.023 | 0.035 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".