Dealing With Scam in Online Qualitative Research: Strategies and Ethical Considerations
Bibliographic record
Abstract
In the wake of COVID-19, numerous research projects moved to online data collection to comply with public health guidelines. Since then, many qualitative projects have continued to use online methods to collect data. While online methods facilitated research continuity, they also introduced new opportunities for deceptive behaviors, particularly misrepresentation and multiple participation. Drawing from a recent project that conducted online interviews with young people who detransition after a gender transition, this article describes how fraudulent interviews were identified and dealt with. We present 12 indicators of potential scams in qualitative interviews, including similarities between participants, the type of information provided, participants’ behaviors, and inconsistencies in the narratives. We discuss our overall experience and, in light of recent literature, present strategies to prevent and deal with scams in qualitative research.
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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 | 0.580 | 0.515 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.020 | 0.043 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.010 | 0.021 |
| Research integrity | 0.015 | 0.019 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".