Pics, Dicks, Tits, and Tats: negotiating ethics working with images of bodies in social media research
Bibliographic record
Abstract
With the rise of camera-enabled cellphones and social media platforms that focus on vernacular images (e.g. Instagram ™ and Snapchat ™ ), researchers and intuitional ethics boards increasingly seek guidelines for research using digital images of bodies shared on social media. This article presents the findings of in-depth interviews with 16 researchers who have received institutional ethics approval to study images of bodies shared on social media platforms. The interviews explored the researchers’ (a) processes of selecting their methodologies, (b) experiences getting institutional ethics approval, and (c) personal research ethics that emerged through their research programs. The findings indicate that researchers and review boards generally lack resources. Researchers often adhered to contextual integrity, were protective while not patronizing, and adopted a feminist materialist ethics of care, which included consideration of the manifold human and nonhuman forces at play in the lifespan of images in digital research. Researchers also practiced strategies like ongoing consent, “ethics-on-the-go,” ethical visual fabrication, and conscious omission.
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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 | 0.102 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.029 | 0.105 |
| Scholarly communication | 0.020 | 0.025 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".