“I’ve Been Silenced for so Long”: Relational Engagement and Empowerment in a Digital Storytelling Project With Young Women Exposed to Dating Violence
Bibliographic record
Abstract
Despite decades of research identifying the myriad causes and consequences, young women continue to be exposed to a variety of abuses in their dating relationships. Those who experience such violence often feel shame and isolation and hesitate to reach out for support for fear that their stories will not be heard, respected, or garner appropriate responses. Such abuse often results in grave consequences to well-being and quality of life, with the risk of exposure to one incident of abuse potentially leading to a cycle where young women may be repeatedly drawn to abusive relationships. Finding new ways to expose and disrupt this cycle of abuse in intimate relationships is critical. This article highlights the methods used, specifically an adapted version of digital storytelling as a potential empowerment research methodology with a small group of young women exposed to dating violence. Implementation of this methodology occurred in four phases: providing methodological context, preparing (setting the stage), implementing (constructing and sharing digital stories), and evaluating (experience and impact). Each phase of the methodology is described along with lessons learned to advance the innovative use of digital storytelling in anti-violence research.
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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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.012 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".