Rainfall, temperature, and Classic Maya conflict: A comparison of hypotheses using Bayesian time-series analysis
Bibliographic record
Abstract
Studies published over the last decade have reached contrasting conclusions regarding the impact of climate change on conflict among the Classic Maya (ca. 250-900 CE). Some researchers have argued that rainfall declines exacerbated conflict in this civilisation. However, other researchers have found that the relevant climate variable was increasing summer temperatures and not decreasing rainfall. The goal of the study reported here was to test between these two hypotheses. To do so, we collated annually-resolved conflict and climate data, and then subjected them to a recently developed Bayesian method for analysing count-based times-series. The results indicated that increasing summer temperature exacerbated conflict while annual rainfall variation had no effect. This finding not only has important implications for our understanding of conflict in the Maya region during the Classic Period. It also contributes to the ongoing discussion about the likely impact of contemporary climate change on conflict levels. Specifically, when our finding is placed alongside the results of other studies that have examined temperature and conflict over the long term, it is clear that the impact of climate change on conflict is context dependent.
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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 | 0.042 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".