Bots, scammers, and fraudulent responders: a year of disrupted data collection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Reports of survey bots disrupting data collection began to appear in the early 2010s, and the degree to which they and other types of fraudulent responders have infiltrated the psychology research literature has only increased since then. Some investigators have found that up to 94% of the responses they have received are fraudulent or invalid, increasing the resources needed to collect accurate data and compromising the integrity of research findings. Flawed results could be used as the basis for ineffective or even harmful policies or interventions. In this paper, we describe three projects, including two online surveys and one interview-based study, and the challenges we experienced with fraudulent participation. We detail the strategies used and their degree of success, including restricting access to surveys, enabling online platform protections, using trick questions and attention checks, evaluating response characteristics manually , and combinations thereof. Implications for future research design and ethical considerations are explored.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it