Online recruitment and testing of infants with Mechanical Turk
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
Testing infants in the laboratory is expensive in time and money; consequently, many studies are underpowered, reducing their reproducibility. We investigated whether the online platform, Amazon Mechanical Turk (MTurk), could be used as a resource to more easily recruit and measure the behavior of infant populations. Using a looking time paradigm, with users' webcams we recorded how long infants aged 5 to 8months attended while viewing children's television programs. We found that infants (N=57) were more reliably engaged by some movies than by others and that the most engaging movies could maintain attention for approximately 70% of a 10- to 13-min period. We then identified the cinematic features within the movies. Faces, singing-and-rhyming, and camera zooms were found to increase infant attention. Together, we established that MTurk can be used as a rapid tool for effectively recruiting and testing infants.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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