Gender differences in BaYaka forager sleep-wake patterns in forest and village contexts
Bibliographic record
Abstract
Sleep studies in small-scale subsistence societies have broadened our understanding of cross-cultural sleep patterns, revealing the flexibility of human sleep. We examined sleep biology among BaYaka foragers from the Republic of Congo who move between environmentally similar but socio-ecologically distinct locations to access seasonal resources. We analyzed the sleep-wake patterns of 51 individuals as they resided in a village location (n = 39) and a forest camp (n = 23) (362 nights total). Overall, BaYaka exhibited high sleep fragmentation (50.5) and short total sleep time (5.94 h), suggestive of segmented sleep patterns. Sleep duration did not differ between locations, although poorer sleep quality was exhibited in the village. Linear mixed effect models demonstrated that women's sleep differed significantly from men's in the forest, with longer total sleep time (β ± SE = - 0.22 ± 0.09, confidence interval (CI) = [- 0.4, - 0.03]), and higher sleep quality (efficiency; β ± SE = - 0.24 ± 0.09, CI = [- 0.42, - 0.05]). These findings may be due to gender-specific social and economic activities. Circadian rhythms were consistent between locations, with women exhibiting stronger circadian stability. We highlight the importance of considering intra-cultural variation in sleep-wake patterns when taking sleep research into the field.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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 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".