Getting the picture and changing the picture: visual methodologies and educational research in South Africa
Bibliographic record
Abstract
At the risk of seeming to make exaggerated claims for visual methodologies, what I set out to do is lay bare some of the key elements of working with the visual as a set of methodologies and practices. In particular, I address educational research in South Africa at a time when questions of the social responsibility of the academic researcher (including postgraduate students as new researchers, as well as experienced researchers expanding their repertoire of being and doing) are critical. In so doing I seek to ensure that the term "visual methodologies" is not simply reduced to one practice or to one set of tools, and, at the same time, to ensure that this set of methodologies and practices is appreciated within its full complexity. I focus on the doing, and, in particular, on the various approaches to doing through drawings, photo-voice, photo-elicitation, researcher as photographer, working with family photos, cinematic texts, video production, material culture, advertising campaigns as nine key areas within visual methodologies.
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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 | 0.050 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.019 | 0.052 |
| Scholarly communication | 0.021 | 0.020 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".