Ethical Processes and Dilemmas during Research with Youth on Cyber-Risk
Bibliographic record
Abstract
In this article, we reflect on the ethical processes and dilemmas we encountered in almost a decade of qualitative research with teenagers about digital technologies and cyber-risk. Our research underscores both the opportunities and challenges of teenagers’ engagements with digital technologies, including cyberbullying and image-based sexual harassment and abuse (i.e., non-consensual sexting), on popular social media platforms. Our current research explores teenagers’ experiences with cyber-risk during the COVID-19 pandemic, including managing homeschooling (due to lockdowns), online addiction, mental health challenges, and encounters with disinformation and misinformation. We discuss our experiences with focus group facilitation and one-to-one semi-structured interviews, specifically our reflections on ethical processes encountered in the field, such as fostering rapport with young participants given the significant age gaps and our lack of knowledge at times, regarding digital technologies or topics like image-based sexual abuse. We also discuss our experiences conducting research with teenagers under the new capacity to consent ethical framework, which positions children and youth as often having agency to consent to research independently from their parents or legal guardians. Here, we detail reflections on navigating a new approach and highlight some of the considerations arising from ascertaining assent and consent. Centralizing issues of developing rapport, trust, and ethical processes related to interactional dynamics during interviews, the paper provides insights and possible strategies for those conducting research with children and youth.
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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 | 0.251 | 0.198 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.030 | 0.081 |
| Scholarly communication | 0.020 | 0.018 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".