The Textual-Visual Thematic Analysis: A Framework to Analyze the Conjunction and Interaction of Visual and Textual Data
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
Visual methods offer an innovative approach to qualitative research through their potential to prompt dialogue, enrich verbal and textual data, and enable participants to communicate about difficult topics. However, the use of visual methods requires that researchers rethink methodological aspects of data generation and analysis, especially when working with participant-generated images. Although there are now many analytical frameworks and guidebooks providing instructions on the analysis of textual and visual materials, detailed descriptions of how these elements are brought together are often missing from research reports, precluding novice and other researchers from understanding how findings were attained. Our aim in this article is to describe and illustrate the Textual-Visual Thematic Analysis (TVTA), a framework we developed to collaboratively analyze the conjunction and interaction of textual and visual data in a photo-elicitation study. Given that the ethical and methodological aspects are deeply entwined, we begin the article by contextualizing the data obtained from the photo-elicitation study and then consider confidentiality and approaches to valuing participants' voices. Next, we share the TVTA framework, its procedural implementation, and insights derived from evolving our data analysis approach. We conclude by offering reflections on the limitations and possibilities for future research.
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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 | 0.045 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it