Achieving sustainable societies: Lessons from modelling the ancient Maya
Bibliographic record
Abstract
The ancient Maya provide an example of a complex social-ecological system which developed impressively before facing catastrophic reorganization. In order for our contemporary globally-connected society to avoid a similar fate, we aim to learn how the ancient Maya system functioned, and whether it might have been possible to maintain resilience and avoid collapse. The MayaSim computer model was constructed to test hypotheses on whether system-level interventions might have resulted in a different outcome for the simulated society. We find that neither collapse nor sustainability are inevitable, and the fate of social-ecological systems relates to feedbacks between the human and biophysical world, which interact as fast and slow variables and across spatial and temporal scales. In the case of the ancient Maya, what is considered the �peak� of their social development might have also been the �nadir� of overall social-ecological resilience. Nevertheless, modelling results suggest that resilience can be achieved and long-term sustainability possible, but changes in sub-systems need to be maintained within safe operating boundaries.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".