Tech-facilitated violence: thinking structurally and intersectionally
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it