The instruction is to tell someone: uses of disclosures of gender-based violence post #metoo
Bibliographic record
Abstract
Since its emergence, the #metoo movement has elicited countless narratives. People with experiences of gender-based violence are encouraged to perform their stories for the public or realms of networked publics or counterpublics. This research-creation project is a collaborative investigation into the process of disclosure and how telling a story of violence varies across time and setting, impacting the formation of subjectivity and cultural positions of victim or survivor. The project was installed in an empty city lot as a silent moving-image of the spectrographic data of two voices in dialogue. The artwork displays only visual sonic information like rhythm, timbre, pitch, and harmonics, as well as the space of silence and listening while the narrative content is muted. The piece’s silence enacts a melancholic resistance to the prevalence of survivor narratives that uphold neoliberal ideals of personal expression and effort as avenues for healing and wholeness. It also attends to the importance of dynamic dialogue and to the necessity of an engaged listening partner to a person’s telling. This accompanying project-paper summarizes the project’s methodology, creation, and installation. It engages with theories of sound and listening, trauma, resilience, melancholy, and commitments to ethical engagement with non-violence. This project paper investigates the varied uses of disclosures of gender-based violence under neoliberal white supremacist hetero-patriarchy and how the spectacle of overcoming is utilized to uphold systems of gendered power relations and socio-economic inequality.
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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 | 0.008 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".