Reflections on Intersectional Feminist Approaches: Promoting an Ethics of Care in Research on Technology-Facilitated Sexual Violence and Youth
Bibliographic record
Abstract
Technology-facilitated sexual violence (TFSV) is increasing in prevalence among young people and has significant implications for their mental health and well-being. In this article, we reflect on our experiences taking an intersectional feminist approach to research on TFSV among youth. In the feminist tradition of reflection, we critically examine how our intersectional feminist approach challenged and/or aligned with typical psychological research. Using Wigginton and Lafrance's methodological considerations for doing critical feminist research in psychology, we consider questions related to reflexivity, representation, language, and the value of mobilizing research for social change. We hope to offer tangible examples and strategies that scholars across disciplines can take when applying intersectional feminist and values-based approaches to their research, especially with challenging topics such as youth mental health and sexual violence.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".