Pics, Dicks, Tits, and Tats: negotiating ethics working with images of bodies in social media research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it