Bibliographic record
Abstract
Technologically-facilitated violence (TFV) can take many shapes and forms, In this thought piece, we reflect on TFV from structural and intersectional perspectives, examining how these might change our understanding of TFV, with particular attention to gender-based TFV. We are motivated to engage in this reflection for two main reasons. First, traditional understandings of violence, including gender-based violence, tend to prioritise physical acts (whether in word or in application), contributing to a trivialisation of the kinds of harms effected through digitised communications networks (Dunn, 2021). Second, if TFV is understood primarily in terms of individual interpersonal acts, our ability to understand how intersecting oppressions such as sexism, racism, homophobia, transphobia, colonialism affect the likelihood of being targeted and the experience of violence will be compromised. As Black feminist and critical race scholars such as Crenshaw (1991), Hill Collins (2017), and Jiwani, Berman and Cameron (2010) have ably demonstrated, individualistic single axis accounts of violence outside of technologised contexts have resulted in exclusionary and dangerous outcomes that selectively harm members of equality-seeking communities. The result of these individualised understandings of violence is that structural oppressions are ‘erased, trivialised, or contained within categories that evacuate the violation of [structural] violence’ (Jiwani, 2006, xi–xii). Among other effects, such erasure risks rendering invisible opportunities to intervene with respect to violence not carried out by individuals, often resulting in ‘remedies’ that emphasise interventions by the state against individual actors (for example, through criminal law), powers that already disproportionately target members of equality-seeking communities, and misses the potential need to intervene on capitalistic corporate systems and behaviours. In both cases, the prospect of achieving justice recedes.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.012 | 0.068 |
| Scholarly communication | 0.031 | 0.039 |
| Open science | 0.004 | 0.026 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 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".