Demographic shifts, inter-group contact, and environmental conditions drive language extinction and diversification
Bibliographic record
Abstract
Humans currently collectively use thousands of languages1,2. The number of languages in a given region (i.e. language “richness”) varies widely3–7. Understanding the processes of diversification and homogenization that produce these patterns has been a fundamental aim of linguistics and anthropology. Empirical research to date has identified various social, environmental, geographic, and demographic factors associated with language richness3. However, our understanding of causal mechanisms and variation in their effects over space has been limited by prior analyses focusing on correlation and assuming stationarity3,8. Here we use process-based, spatially-explicit stochastic models to simulate the emergence, expansion, contraction, fragmentation, and extinction of language ranges. We varied combinations of parameter settings in these computer-simulated experiments to evaluate the extent to which different processes reproduce observed patterns of pre-colonial language richness in North America. We find that the majority of spatial variation in language richness can be explained by models in which environmental and social constraints determine population density, random shocks alter population sizes more frequently at higher population densities, and population shocks are more frequently negative than positive. Language diversification occurs when populations split after reaching size limits, and when ranges fragment due to population contractions following negative shocks or due to contact with other groups that are expanding following positive shocks. These findings support diverse theoretical perspectives arguing that language richness is shaped by environmental and social conditions, constraints on group sizes, outcomes of contact among groups, and shifting demographics driven by positive innovations, such as new subsistence strategies, or negative events, such as war or disease.
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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 | 0.000 | 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) | 0.001 | 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".