Decline and Fall, Growth and Spread, or Resilience? Approaches to Studying How and Why Societies Change
Bibliographic record
Abstract
Abstract: Academic and popular libraries are filled with works seeking to uncover why some past societies collapsed, or to explain why others flourished, or why a few prove resilient against the major shocks and disasters that have doomed so many others. While we have learned much from this diverse set of scholarship, there are still many questions about how these different aspects of societal dynamics relate to each other and we lack clear consensus on the best approach(es) to provide answers. Further, historical precedents of collapse, growth, and resilience are being sought to help craft effective responses to the forces that threaten our contemporary world, from climate change to the risk of global pandemics to rising inequality and sectarian conflict. This essay reviews recent and prominent approaches to these topics and provides some thought on how broad, holistic approaches to studying societal dynamics can shed light on these pressing issues.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.020 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".