Evaluating CloudResearch’s Approved Group as a Solution for Problematic Data Quality on MTurk
Bibliographic record
Abstract
Maintaining data quality on Amazon Mechanical Turk (MTurk) has always been a concern for researchers. CloudResearch, a third-party website that interfaces with MTurk, assessed ~100,000 MTurkers and categorized them into those that provide high- (~65,000, Approved) and low-(~35,000, Blocked) quality data. Here, we examined the predictive validity of CloudResearch’s vetting. Participants (N = 900) from the Approved and Blocked groups, along with a Standard MTurk sample, completed an array of data quality measures. Approved participants had better reading comprehension, reliability, honesty, and attentiveness scores, were less likely to cheat and satisfice, and replicated classic experimental effects more reliably than Blocked participants who performed at chance on multiple outcomes. Data quality of the Standard sample was generally in between the Approved and Blocked groups. We discuss the implications of using the Approved group for scientific studies conducted on Mechanical Turk.
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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 | 0.103 | 0.370 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".