SELF-REPRESENTATION IN PARTICIPATORY VIDEO RESEARCH
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Participatory video involves co-researchers using digital or video cameras to create their own videos and present issues according to their sense of what is important. In 2018, the authors—including three co-researchers from refugee backgrounds—collaborated through participatory video research to document views on better access and participation in higher education. Here, we reflect on key ethical issues encountered and share lessons learnt from our project. Our aim is not to discredit this methodology but to contribute new discussions on how participatory video can be used effectively as a form of self-representation to target wide audiences and effect social and policy change. This way, debates on the social and political potentialities of arts-based methods such as participatory video can be expanded. Since deploying participatory video in forced migration research is a relatively novel approach, there is much scope to expand the contours of knowledge on its potential to reach diverse audiences and open up new opportunities for social and political impact.
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.
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 | 0.039 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it