Intersectional Perspectives on Technology-Facilitated Sexualized Violence: A Mixed-Methods Investigation of Postsecondary Institution Materials
Bibliographic record
Abstract
Technology-facilitated sexualized violence (TFSV) is a growing concern in educational, public health, and public policy spaces, with severe implications for health and well-being. In particular, young adults are at particularly high risk of TFSV victimization, which is compounded by structural sexism, heterosexism, colonialism, racism, and additional forms of oppression. Applied research on current TFSV educational awareness, prevention, and intervention materials/resources available for postsecondary students and employees (e.g. staff, administrators, and faculty) is lacking. In late 2022-early 2023, we conducted an environmental scan of TFSV resources at 25 public postsecondary institutions (PSIs) in British Columbia, Canada. The purpose was to identify TFSV-specific institutional materials/resources including support services for TFSV victim-survivors and educational training/resources on TFSV victimization (e.g. response workshops, awareness campaigns, etc.). Content analyses of identified TFSV resources examined intersectional considerations. We identified an overwhelming lack of TFSV-specific resources, with only one PSI indicating any such resources. Follow-up interviews with PSI employees in sexualized violence responding roles (N = 6) confirmed a dearth of institutional TFSV information and resources. We identified a pressing need for additional funding to support the development and implementation of TFSV-specific resources, particularly those incorporating intersectional frameworks. To reduce the harms associated with TFSV, which are shaped by systems of oppression, we call for the development of “living” TFSV resources, centralization of these resources, increased funding for professional development and policy implementation, and the incorporation and valuation of intersectional praxis at all stages of policy development and implementation.
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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 | 0.021 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".