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Record W4388241913 · doi:10.52306/2578-3289.1036

An Exploratory Perception Analysis of Consensual and Nonconsensual Image Sharing

2019· article· en· W4388241913 on OpenAlexaff
Jin Ree Lee, Steven M. Downing

Bibliographic record

VenueInternational Journal of Cybersecurity Intelligence and Cybercrime · 2019
Typearticle
Languageen
FieldPsychology
TopicSexuality, Behavior, and Technology
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsPsychologyPerceptionThematic analysisSocial psychologyImage sharingExploratory researchQualitative researchImage (mathematics)SociologyComputer science

Abstract

fetched live from OpenAlex

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).

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.203
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.038
GPT teacher head0.377
Teacher spread0.339 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2019
Admission routes1
Has abstractyes

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