Re/formulating Ethical Issues for Visual Research Methods
Bibliographic record
Abstract
This paper discusses six categories of key ethical issues that are important to consider when using visual methods in social research. The categories were identified during workshop discussions with researchers working across disciplines and using a range of visual methods. They have been used to inform guidelines for the ethical conduct of research using visual methods. The categories represent both familiar and emerging ethical challenges. They include widely accepted strategies for meeting ethical obligations to ensure participants’ informed consent, to maintain confidentiality, and to design and conduct research that minimises harm. Three further categories represent more novel ethical issues that are particularly prominent in visual methods: managing fuzzy boundaries around the multiple purposes that visual research may serve, addressing questions of authorship and ownership of visual products generated during research, and dealing with representation and audiences when disseminating research findings. In this paper we reflect on the tensions and challenges these issues raise for researchers working with visual methods, and consider potential strategies to address these challenges. By identifying and critiquing ethical issues that are prominent in visual methods, this paper contributes to a growing body of work that aims to ensure the ethical conduct of visual research.
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.
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 | 0.544 | 0.588 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.015 | 0.102 |
| Scholarly communication | 0.030 | 0.034 |
| Open science | 0.008 | 0.022 |
| Research integrity | 0.016 | 0.030 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".