How Do We ‘See’ Occupations? An Examination of Visual Research Methodologies in the Study of Human Occupation
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
This article argues that visual research methodologies have potential to contribute to the study of occupation. The use of visual research methodologies is quickly growing in a number of disciplines and can help researchers to access information and reasoning not accessible through interview, log or survey. The reflexive, reflective, engaged process of creating and analysing visual materials allows for rich representations on behalf of participants, and immersion in the data on the part of researchers. This paper explores photovoice, body mapping and textual analysis of visual materials to understand how they can contribute to occupational science research. These methods were chosen because they represent the current methods being used by researchers in visually-based research literature. It is argued that when used appropriately, the addition of visual research methodologies to occupational science research will help researchers access rich and authentic information, and that visuals can represent many layers of meaning that may otherwise be lost in a conversation, log, or piece of historical literature.
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 | 0.088 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.000 |
| 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