Doing things otherwise: Liveability as an epistemic reorientation to interdisciplinary work
Bibliographic record
Abstract
Abstract In this paper, I critically rethink how interdisciplinary approaches to artificial intelligence (AI) are framed, particularly in relation to technology-facilitated gender- and sexuality-based violence (TFGSBV). While collaboration across disciplines is often positioned as a solution to AI-related harms, I argue that these approaches can reproduce the very exclusions they aim to address. Drawing on my work with the Feminist Internet Research Network and dialogues with researchers and activists across the Majority Worlds, I propose liveability – not only as a principle, but as a practice – for interdisciplinary work. Liveability refers to the conditions that allow individuals and communities to thrive – emotionally, politically and socially – even amid structural violence and technological harm. I treat interdisciplinarity not as a goal in itself, but as an ethical and relational process grounded in care, accountability and situated knowledge. Through feminist and queer perspectives, I examine how AI harms are often abstracted into technical problems, and I advocate for slower, more grounded practices that centre lived experience. Through lessons from Feminist Internet Research Network’s multistakeholder collaborations, I show how introducing liveability into interdisciplinary and multistakeholder work – particularly around TFGSBV – enables alternative, more inclusive forms of collaboration. Liveability becomes a compass for working across difference, shifting interdisciplinary AI research towards justice, community and collective transformation.
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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 | 0.055 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.017 | 0.167 |
| Scholarly communication | 0.026 | 0.032 |
| Open science | 0.003 | 0.032 |
| Research integrity | 0.008 | 0.012 |
| 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".