How are mortality rates affected by population density?
Bibliographic record
Abstract
Biologists have found that the death rate of cells in culture depends upon their spatial density. Permanent "Stay alive" signals from their neighbours seem to prevent them from dying. In a previous paper (Wang et al. 2013) we gave evidence for a density effect for ants. In this paper we examine whether there is a similar effect in human demography. We find that although there is no observable relationship between population density and overall death rates, there is a clear relationship between density and the death rates of young age-groups. Basically their death rates decrease with increasing density. However, this relationship breaks down around 300 inhabitants per square kilometre. Above this threshold the death rates remains fairly constant. The same density effect is observed in Canada, France, Japan and the United States. We also observe a striking parallel between the density effect and the so-called marital status effect in the sense that they both lead to higher suicide rates and are both enhanced for younger age-groups. However, it should be noted that the strength of the density effect is only a fraction of the strength of the marital status effect. In spite of the fact that this parallel does not give us an explanation by itself, it invites us to focus on explanations that apply to both effects. In this light the "Stay alive" paradigm set forth by Prof. Martin Raff appears as a natural interpretation. It can be seen as an extension of the "social ties" framework proposed at the end of the 19th century by the sociologist Emile Durkheim in his study about suicide.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".