NOVEL, EDUCATIONAL AND LEGAL RESPONSES TO TECHNOLOGY-FACILITATED SEXUAL VIOLENCE
Bibliographic record
Abstract
The three panel presenters and session chair are co-researchers in a seven-year research partnership—involving 28 educational institutions, 25 co-investigators,15 community partners, and 50+ students—that aims to address sexual violence in physical and virtual forms in university contexts across Canada and internationally. The project specifically seeks to address, dismantle and prevent sexual violence by means of multi-sector partnership solutions across the fields of education, law, policy, arts, popular culture, health care, management, news and social media. Using the methodological framework of Parallaxic Praxis (Sameshima et al., 2019), the team looks at a phenomenon from different perspectives by using varied methodological processes as well as a range of rigorous methods of encoding, decoding, and rendering data; and establishing post-qualitative possibilities for generating and mobilizing knowledge to broader audiences. The juxtaposition of renderings (constructions made from deep analysis of the phenomena such as papers, presentations, artworks, and other artefacts), when presented together, manufacture a dynamic agency between works capturing intertextualities, aporias, choruses, and a poesis that arise in the coalescence of the unassimilated, individual investigations. In this panel, an overview of the larger project and the significant milestones in the first four years specifically related to internet technologies will be provided. Drawing from multi-perspectives, the second presenter will address image-based sexual abuse and copyright in Canada, and the third will share data collected from this project in the form of excerpts from an epistolary novel. The session demonstrates how multi-modal investigations and dissemination offer possibilities for extending knowledge production.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.028 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 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".