The Face as Image: Witnessing, Minimizing Harm and Testimony
Bibliographic record
Abstract
This paper explores the human face as an important image, and as visual data, in the context of academic work. We explore its philosophical value through a discussion of testimony and witnessing, visual data through the interface between academia and social media in the broader world, and with a reflection of university-based ethics. We explore the example of academic publication of a participant’s face as presented in peer reviewed journals, online and open access, or through other academic deliverables and printed content. The question of how we as researchers and members of REBs (Research Ethic Boards) should act as protectors of participants who want to reveal their faces as image data in research is an interesting one. The paper explores the context of ‘image testimony’, in light of literature on witnessing and trends in social media. The face as an image online has become an important “voice”. The face as visual image then also emerges as relevant in ethics, through data collection, and the ongoing work of researchers. In the context of modern digital media communications, it is hard to imagine the success of social media platforms without human faces, personal choices made by individuals posting selfies while eating, demonstrating, or going on nature hikes. In the broadening cultural narrative of the face as digital image, we reflect on where the university and the REB stand. Our discussion explores ethical considerations and makes recommendations.
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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 | 0.025 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.026 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".