Pandemics and Systemic Discrimination: Technology-Facilitated Violence and Abuse in an Era of COVID-19 and Antiracist Protest
Bibliographic record
Abstract
Abstract Technology-facilitated violence and abuse is a truly global problem. As the diverse perspectives and experiences featured in this book have shown, the deep entanglement between technologies, inequality, marginalization, abuse, and violence require multi-faceted and collaborative responses that exist within and beyond the law. When this chapter was written, society was (and continues to be) facing an unprecedented challenge in COVID-19 – a global pandemic. At the same time, a renewed focus on racist police and civilian violence has occurred following the killings of George Floyd, Ahmaud Arbery, and Breonna Taylor in the United States. As we describe in this chapter, these two major moments are ongoing reminders of the profound social inequalities within our global communities, which are grounded in systemically discriminatory oppressions and their intersections. This chapter draws together some thoughts on technology-facilitated violence and abuse in an era of COVID-19 and antiracist protest. It explores these within the context of the book as a whole, highlighting the importance for improved understanding of, and responses to, technology-facilitated violence and abuse as part of a broader push for social justice.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".