A reflection on research ethics and citizen science
Bibliographic record
Abstract
Ethics review of research involving humans has become something of an institution in recent years. It is intended to protect participants from harm and, to that end, follows rigorous standards. Given recent changes in research methodologies utilized in medical research, it may be that ethics review for some kinds of studies needs to be reexamined. The purpose of this paper is to stimulate dialogue regarding the kind of review required for citizen science-based research. We describe a case study of a proposal submitted to our research ethics board and propose different approaches to proportionate review in research involving citizen scientists. In particular, we describe how problems with the term “participant” led to confusion in review of this study and examine the study in light of current Canadian guidelines. We suggest that the term participant and indeed the general approach to low-risk community-based studies such as the one described warrant reexamination.
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 | 0.206 | 0.176 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.123 |
| Scholarly communication | 0.026 | 0.033 |
| Open science | 0.006 | 0.018 |
| Research integrity | 0.058 | 0.106 |
| 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".