It’s raining bots: how easier access to internet surveys has created the perfect storm
Bibliographic record
Abstract
Online surveys are an increasingly common way to collect data from the public, with social media and financial incentives (e.g. gift cards) commonly used to increase participation rates. Anonymity, ease of response, and the potential to reach diverse demographics have also contributed to the popularity of online surveys. Health services research benefits from the increased accessibility that online survey-based data collection provides; however, fraudulent responses are of concern. The following article describes our team's experience with a national survey of Canadian healthcare providers being overrun with fraudulent responses and approach to ensure the validity of our survey data. We provide recommendations for research teams on how best to design their surveys, work with their institutions to implement safeguards within survey platforms, and screen completed responses. We also describe fraudulent open-text responses that we believe to have been produced with the help of artificial intelligence and are sounding the alarm for other researchers to be aware of this potential threat to data integrity. Informed by the learnings shared, researchers and research institutions can be better equipped to prevent and screen fraudulent responses to continue successfully engage the public in online 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 | 0.119 | 0.313 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.017 | 0.025 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.021 | 0.008 |
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".