Ethical Dilemmas in Research on Internet Communities
Bibliographic record
Abstract
There has been a rapid growth in the number of articles using Internet data sources to illuminate health behavior. However, little has been written about the ethical considerations of online research, especially studies involving data from Internet discussion boards. Guidelines are needed to ensure ethical conduct. In this article, the authors examine how a youth-focused research program negotiated ethical practices in the creation of its comprehensive health site and online message board. They address three situations in which ethical predicaments arose: (a) enrolling research participants, (b) protecting participants from risk or harm, and (c) linking public and private data. Drawing on the ethical principles of autonomy, nonmaleficence, justice, and beneficence, the authors present practical guidelines for resolving ethical dilemmas in research on Internet communities.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Research integrity Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | MetaresearchResearch integrity Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | medium |
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 | 0.598 | 0.509 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.030 | 0.151 |
| Scholarly communication | 0.026 | 0.026 |
| Open science | 0.007 | 0.025 |
| Research integrity | 0.021 | 0.021 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".