Seeing how it works: A visual essay about critical and transformative research in education
Bibliographic record
Abstract
As visual researchers in the field of education we have initiated and completed numerous participatory projects using qualitative visual methods such as drawing, collage, photovoice, and participatory video, along with organising screenings and creating exhibitions, action briefs, and policy posters. Locating this work within a critical paradigm, we have used these methods with participants to explore issues relating to HIV and AIDS and to gender-based violence in rural contexts. With technology, social media, and digital communication network connections becoming more accessible, the possibilities of using visual participatory methods in educational research have been extended. However, the value of visual participatory research in contributing to social change is often unrecognised. While the power of numbers and words in persuasive and informative change is well accepted within the community of educational researchers, the power of the visual itself is often overlooked. In this visual essay, we use the visual as a way to shift thinking about what it means to do educational research that is transformative in and of itself. As an example we draw on our visual participatory work with 15 first-year women university students in the Girls Leading Change1 project to explore and address sexual violence at a South African university. We aim to illustrate, literally, the possibilities of using the visual, not only as a mode of inquiry, but also of representation and communication in education and social science scholarship.
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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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.045 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.007 |
| 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 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".