How to make a collaborative videography using Phygital affordances to study sensitive topics
Bibliographic record
Abstract
Purpose The physical filmmaking landscape has been transformed by the emergence of digital platforms that foster interaction and dialogue. The accessibility and affordability of mobile production tools have empowered anyone with a mobile phone to become a media content creator. Accordingly, this paper aims to present a multi-method approach for creating phygital projects that involve people as active participants rather than mere subjects who collaborate with the researchers to tell their stories. Design/methodology/approach Research participants can embrace diverse roles, serving as co-researchers, content creators, curators and collaborators. The authors use various engagement strategies with the research participants, who are often marginalized or underrepresented, to encourage their participation and give them agency and creative control. Thus, we also use a participatory action research approach to help advocate for the participants’ facial equality concerns. Findings Collaborative videography embraces the mosaic of voices expressing intricate social issues. In this project, research participants with “facial differences” explain their experiences in facing society. Originality/value By experimenting with participatory frameworks and combining physical interactions (such as in-person meetings) with digital platforms like Zoom and social media, the authors suggest a multi-method approach that honors the authentic stories of the research participants, effectively engages the audience and explains how phygital research methodologies can be used in interpretive consumer research, particularly in co-creating films that capture strong visuals.
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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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".