Why study power in digital spaces anyway? Considering power and participatory visual methods
Bibliographic record
Abstract
In this article, we interrogate notions of power in relation to three participatory visual methods: drawing, photovoice, and making cellphilms (videos made on cell phones). In particular, we address power from the perspectives of Foucault, Freire, Giroux, and hooks in a consideration of the power structures operating in and around participatory visual research. We seek to understand the power dynamics that operate in participatory visual research—particularly in relation to digital media. In so doing, we foreground the notion of power in a discussion of a workshop on participatory visual methodologies that we conducted as part of a graduate student conference. Since participatory visual research artifacts can be both created and disseminated through digital spaces, this work offers implications for researchers working in this field. We conclude that more theoretical work needs to be done to enable us to articulate more fully the power dynamics at play in participatory visual 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 | 0.081 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.011 | 0.094 |
| Scholarly communication | 0.017 | 0.037 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".