How Do We ‘See’ Occupations? An Examination of Visual Research Methodologies in the Study of Human Occupation
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.
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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 | 0.071 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.012 | 0.053 |
| Scholarly communication | 0.021 | 0.017 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".