Exploring ethical frontiers of visual methods
Bibliographic record
Abstract
Visual research is a fast-growing interdisciplinary field. The flexibility and diversity of visual research methods are seen as strengths by their adherents, yet adoption of such approaches often requires researchers to negotiate complex ethical terrain. The digital technological explosion has also provided visual researchers with access to an increasingly diverse array of visual methodologies and tools that, far from being ethically neutral, require careful deliberation and planning for use. To explore these issues, the Symposium on Exploring Ethical Frontiers of Visual Methods was held at the University of Melbourne, Australia, on 4 March 2014. The symposium was hosted by the Visual Research Collaboratory, a consortium of Australian and Canadian visual researchers, with support from Melbourne Social Equity Institute, University of Melbourne. The symposium represented the culmination of a process to develop a resource outlining principles of ethical practice for visual researchers and ethics committee members, the Guidelines for Ethical Visual Research Methods, which were launched at the event. The Guidelines present a framework for considering ethical matters in visual research, distinguishing six groups of issues united by an overarching theme: confidentiality; minimizing harm; consent; fuzzy boundaries; authorship and ownership; and representation and audiences.
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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 | 0.447 | 0.465 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.014 | 0.098 |
| Scholarly communication | 0.032 | 0.027 |
| Open science | 0.004 | 0.024 |
| Research integrity | 0.011 | 0.018 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".