Automated Measures of Force and Motion Can Improve Our Understanding of Infants’ Motor Persistence
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
Every day, young learners are confronted with challenges. The degree to which they persist in overcoming those challenges, and the different ways they persist, provides critical insights into the various cognitive, motoric, and affective processes that drive behavior. Here, we present a systematic overview of the methodologies that have been traditionally used to study persistence, and offer suggestions for new approaches to the study of persistence that will make strides in moving the field forward. We argue that automated measures of force and motion, which have long been used in the study of infants’ motoric behavior, can provide a means to unravel the psychological processes that guide infants’ trying behavior. To illustrate this, we present a case study that highlights the novel lessons to be learned by the use of automated measures of force and motion regarding infants’ persistence, along with an analysis of the benefits and drawbacks of this approach, as well as detailed instructions for application. In sum, we conclude that these measures, when used in conjunction with more traditional approaches, will provide creative new insights into the nature and development of early persistence.
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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 | 0.000 |
| 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 | 0.000 |
| 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