Decline and Fall, Growth and Spread, or Resilience? Approaches to Studying How and Why Societies Change
Bibliographic record
Abstract
An avid reader of history will be quite familiar with the rich, emotive narratives detailing the tragic decline and ultimate fall of once mighty civilizations; Rome succumbing to barbarian hordes, Alexander of Macedon’s and Chinggis Khan’s spear-won empires splitting into warring factions, and the demise of the great Inca or Maya civilizations are just a few such examples. On the other side of the stacks, similarly grandiose narratives document some group’s incredible growth and spread taking over vast territories and populations. These tell typically of societies coming to dominate a region, often in the face of overwhelming odds and tribulation or through some precocious development of a key technology or strategy that later becomes widespread. Here, I take stock of previous approaches to studying function – from growth and development to crisis and collapse to resilience – and ask what is the most fruitful lens with which to view fluctuations in how societies function and change over time, as this review essay attempts to accomplish.
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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".