Internet research ethics and the policy gap for ethical practice in online research settings
Bibliographic record
Abstract
A growing number of education and social science researchers design and conduct online research. In this review, the Internet Research Ethics (IRE) policy gap in Canada is identified along with the range of stakeholders and groups that either have a role or have attempted to play a role in forming better ethics policy. Ethical issues that current policy and guidelines fail to address are interrogated and discussed. Complexities around applying the human subject model to internet research are explored, such as issues of privacy, anonymity, and informed consent. The authors call for immediate action on the Canadian ethics policy gap and urge the research community to consider the situational, contextual, and temporal aspects of IRE in the development of flexible and responsive policies that address the complexity and diversity of internet research spaces.
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 | 0.366 | 0.379 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.039 | 0.149 |
| Scholarly communication | 0.042 | 0.025 |
| Open science | 0.006 | 0.022 |
| Research integrity | 0.016 | 0.022 |
| Insufficient payload (model declined to judge) | 0.005 | 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".