Centering Girls' (Media-Making) Stories: A Pandemic Exploration of Video-Storytellers and their Practices, Personas, and Projects
Bibliographic record
Abstract
This interdisciplinary, feminist-informed research explores racialized tween and teen girls’ video-based storytelling and considers how extracurricular programs can support their media-making. Drawing from youth media cultures and media education scholarship, this work aligns with and builds upon research about community-based youth documentary media-making initiatives and limited yet pivotal scholarship that centres girls’—marginalized girls in particular—experiences, including as media-makers. It was further motivated by the prevailing lack of diverse representation in key creative and leadership roles in media industries, my experiences as a woman working in media, and the paucity of research on Canadian youth and their experiences learning about and making media. My inquiry was underpinned by feminist theory, public pedagogy, and feminist media. Utilizing a qualitative case study design, I designed and facilitated a virtual digital storytelling program in Spring 2021 of the pandemic; four ethnoracially-diverse and marginalized girl-identifying youth from Toronto participated in both the program and research study. Research methods included interviews, vlogs, participant-created media, observational footage, and researcher notes. Analysis involved immersing myself in each participant’s data to holistically consider the creative, technical, and social dimensions of her video-storytelling; I also coded interviews and vlogs to identify themes that united the participants. Inspired by Lange’s (2014) exploration of youth technical identities and Lawrence-Lightfoot’s (1983) narrative portraiture methodology, I crafted a video-storytelling “persona” for each participant, weaving in her own words and media project images. Next, I note the broader significance of relationships and connection as well as video storytelling-specific peer and mentor support for participants. I then discuss their video-making in relation to postfeminist-influenced and video-based social media ecologies and girls’ informal, self-directed media education. This research honours participants’ stories and critically reflects upon the wide-ranging nature of their video-storytelling experiences and approaches. It also offers initial recommendations for girl-centered programs that emphasize community, support skills development, and provide safer spaces for their media-making and learning. I advocate for girl-specific media-making communities of practice—particularly for marginalized girls—as necessary interventions in evolving media industries and culture to more fully include, support, reflect, and represent diverse populations of girls and women and their stories.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".