Demonstrating the Utility of Egocentric Relational Event Modeling Using Focal Follow Data from Congolese BaYaka Children and Adolescents Engaging in Work and Play
Why this work is in the frame
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Bibliographic record
Abstract
Temporal aspects of child and adolescent time allocation in diverse cultural settings have been difficult to model using conventional statistical techniques. A new statistical approach, Egocentric Relational Event Modelling (EREM), allows for the simultaneous modelling of activity frequency, duration, and sequencing. Here, EREM is applied to a focal follow dataset of Congolese BaYaka forager child and adolescent play and work activities. Results show that, as children age, they engage in less frequent and extended play bouts and more frequent and extended work bouts. Bout frequency and duration were a more sensitive measure for early sex differences than overall time allocation. Sequential patterns of work and play suggest that these activities have short-term energetic trade-offs. This article demonstrates that EREM can reveal stable and variable patterns in child development.
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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