Imposter Participants in Synchronous Qualitative Research: A Systematic Scoping Review
Bibliographic record
Abstract
Although the issue of bots and fraudulent participants is well established within quantitative research, in recent years there have been increasing incidences of imposter participants within qualitative research. However, how qualitative researchers conceptualise this challenge and what the perceived impact of these imposter participants are, remains underexplored. This systematic scoping review identified 15 articles published since 2018 addressing the topic of imposter participants and fraudulent data in synchronous qualitative research. The review identified that the majority of current articles are commentaries or case study narratives, with little apparent inter disciplinary engagement. Findings indicate that where recommendations are offered these can be subjective or influenced by discipline, with a lack of an evidence informed approach being adopted. The analysis identified three primary issues for applied qualitative research fields, with threats to data integrity and reliability, threats to research diversity, accessibility and reach, and questions of trust and ethics within research highlighted. Developing evidence-based guidance and ensuring cross-disciplinary engagement will be central to maintaining the relevance, impact, and validity of applied 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 | 0.186 | 0.487 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.031 | 0.025 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.005 | 0.003 |
| 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".