Are humans facing a sleep epidemic or enlightenment? Large-scale, industrial societies exhibit long, efficient sleep yet weak circadian function
Bibliographic record
Abstract
The Centers for Disease Control and Prevention declared sleep-related problems to be a public health epidemic. With the advent of biometric sleep tracking technology taking the sleep lab into the field, the study of human sleep is now global, and these new datasets show contrasting findings. Previous reports suggest sleep in small-scale, non-industrial societies to be short and fragmented yet characterized by greater circadian rhythmicity. However, the role of circadian rhythm indicators in understanding global variations in human sleep patterns remains unclear. We examine population-level sleep studies ( n = 54) using polysomnography and actigraphy to test the sleep restriction epidemic hypothesis, which posits that labour demands and technological disruption in large-scale, industrial societies have reduced sleep duration. We used an actigraphy-generated circadian function index from both non-industrial and industrial societies ( n = 866) to test the circadian mismatch hypothesis, which suggests that poor chronohygiene in regulated environments misaligns circadian rhythms in industrial societies. In rejection of the sleep restriction epidemic hypothesis, our results show that industrial societies experience the longest, most efficient sleep, whereas in support of the circadian mismatch hypothesis, sleepers in non-industrial societies are characterized by the greatest circadian function.
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.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".