Archiving digital activism against sexual violence: the challenges for ethical witnessing in research practice
Bibliographic record
Abstract
In 2017, #MeToo swept the world—calling attention to the pervasiveness of sexual violence. In 2022, we published an article highlighting the “digital footprints” left by previous digital feminist campaigns that we argued made #MeToo intelligible. We concluded with a call for scholars to build an archive of digital feminist activism against sexual violence—a call we took up ourselves. This article presents both a reflection and analysis of one such attempt—hiring six research assistants to collect and archive as many digital feminist campaigns as they could in English, French, Spanish, Hindi, and Mandarin. We reflect on our methodological approach in the initial attempt to build this multi-lingual archive, drawing on the concept of ethical witnessing. We discuss challenges in curating our archive, including the rapidly shifting, ad hoc, and ephemeral nature of digital footprints and constantly updating platforms, changing norms around online ethics, and internet censorship in parts of the world, all of which made creating this archive using our framework of ethical witnessing far more complex than initially envisioned. We argue that current and future attempts to curate digital archives on feminist activism must be flexible and dynamic to ensure ethical witnessing takes place.
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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 | 0.141 | 0.162 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.022 | 0.082 |
| Scholarly communication | 0.035 | 0.037 |
| Open science | 0.004 | 0.026 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 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".