Ethical Issues in Designing Internet-Based Research: Recommendations for Good Practice
Bibliographic record
Abstract
This article presents an overview of internet-based research, highlighting the absence of a standard terminology to define and classify such research. The label internet-based research or online research can cover a diverse range of research designs and methods, involving different degrees of ethical concern regarding privacy, transparency, confidentiality, and security. Although the basic principles of human research ethics (such as respect, integrity, justice, and beneficence) are still applicable in this context, interpreting and applying these principles correctly and protecting the interests of the research participants effectively are not easy to ensure. While the nature of the internet poses challenges of user authentication and confidentiality, the diversity of national laws and codes of ethics poses additional challenges. The article refers to relevant Canadian laws, with which the author is familiar. Finally, a set of recommendations are offered to mitigate the ethical challenges of internet-based research. These include ethical practices such as ensuring transparency while recruitment, considering participants’ expectations about privacy, ensuring legal compliance, using secure communication protocols, obtaining informed and knowledgeable consent, offering participants the opportunity to withdraw from the research and retract their data, and ensuring that data are not used for subsequent non-research purposes.
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 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 | 0.542 | 0.580 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.020 | 0.047 |
| Scholarly communication | 0.039 | 0.049 |
| Open science | 0.013 | 0.024 |
| Research integrity | 0.038 | 0.052 |
| Insufficient payload (model declined to judge) | 0.009 | 0.009 |
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".