An Exploratory Perception Analysis of Consensual and Nonconsensual Image Sharing
Bibliographic record
Abstract
Limited research has considered individual perceptions of moral distinctions between consensual and nonconsensual intimate image sharing, as well as decision making parameters around why others might engage in such behavior. The current study conducted a perception analysis using mixed-methods online surveys administered to 63 participants, inquiring into their perceptions of why individuals engage in certain behaviors surrounding the sending of intimate images from friends and partners. The study found that respondents favored the concepts of (1) sharing images with romantic partners over peers; (2) sharing non-intimate images over intimate images; and (3) sharing images with consent rather than without it. Furthermore, participants were more willing to use their own devices to show both intimate and non-intimate images rather than posting on social media or directly sending others the image files. Drawing on descriptive quantitative and thematic qualitative analysis, the findings suggest that respondents perceive nonconsensual image sharing as being motivated by the desire to either bully, “show off,” or for revenge. In addition, sharing intimate digital images of peers and romantic partners without consent was perceived to be troubling because it is abusive and/or can lead to abuse (when involving peers) and a violation of trust (when involving romantic partners).
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".