Bayesian time-series analyses reveal a complex impact of climate change on Classic Maya conflict
Bibliographic record
Abstract
Researchers disagree about the impact of climate change on conflict among the Maya during the Classic period (ca. 250-900 CE). Some contend that increasing aridity exacerbated conflict, while others have found that increasing temperature ramped up conflict. Here, we report a study in which we sought to resolve this disagreement. We collated annually-resolved conflict and climate data, and then created a Bayesian time-series model for analysing count-based prehistoric and historic data. We carried out three analyses, one covering more or less the whole of the Classic Period (292-900 CE), one focused on the Early Classic (292-600 CE), and one that concentrated on the Late Classic (600-900 CE). Our analyses indicated that climate change likely did impact Classic Maya conflict levels, but our results differed from those of previous studies in two important ways. First, we found that the impact of climate change is only evident during the Late Classic. Second, we found that while increasing summer temperature exacerbated conflict, increasing aridity suppressed it. Thus, our study offers a new, more complex perspective on Classic Maya climate-conflict dynamics. It also has implications for our understanding of other aspects of Classic Maya history and for the debate about the likely impact of the current bout of climate change on conflict levels.
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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".