A ‘journey in feminist theory together’: The <i>Doing Feminist Theory Through Digital Video</i> project
Bibliographic record
Abstract
Doing Feminist Theory Through Digital Video is an assignment I designed for my undergraduate feminist theory course, where students created a short digital video on a concept in feminist theory. I outline the assignment and the pedagogical and epistemological frameworks that structured the assignment (digital storytelling, participatory video and feminist approaches to service learning) before presenting an analysis of its learning outcomes gleaned through interviews with students. I argue that incorporating creative and service learning components into a feminist theory course deepens student learning about praxis and subjectivity because it engages their spirit, mind and body, leading to a sustained engagement with feminist theory and an ability to imagine feminist theory outside of the university classroom. My objective for this paper is to share what I have learned as an educator from this project, and that this will be useful to others interested in adapting this kind of assignment to their own pedagogical contexts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.029 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.038 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.006 | 0.018 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".