Reducing the number of non-naïve participants in Mechanical Turk samples
Bibliographic record
Abstract
Using participants who have been previously exposed to experimental stimuli (referred to as non-naïveté) can reduce effect sizes. The workforce of Amazon's Mechanical Turk is particularly vulnerable to this problem and solutions are usually cost and time inefficient and of mixed effectiveness. In response to this problem and its currently underwhelming solutions, we tested various participant recruitment strategies designed to recruit participants naïve to frequently used experimental stimuli. We collected samples using maximum HIT restrictions (50 for Experiment 1 and 2, 500 for Experiment 2) and TurkPrime's (now CloudResearch) naiveté feature and compared them to samples recruited with standard restrictions (95% HIT approval rating). In these comparisons, we replicated past findings where using nonnaïve (vs. naïve) participants has been shown to reduce effect sizes and affect performance on a variety of tasks (e.g., the Cognitive Reflection Test, a Public Goods Game). We demonstrate that restricting by the maximum number of HITs heavily reduces the number of “experienced” research subjects in samples but necessitates some sacrifice in data quality and collection speed. We discuss the pragmatics of our method, its limitations, and future directions for solving the problem of non-naïveté on Mechanical Turk. For those looking to avoid this issue, we recommend setting a maximum HIT restriction of 50 when recruiting participants.
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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 | 0.093 | 0.229 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.010 |
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".