Lessons for an invisible future from an invisible past: Risk and resilience in deep time
Bibliographic record
Abstract
The interrelated concepts of risk and resilience are inherently future-focused. Two main dimensions of risk are the probability that a harmful event will happen in the future and the probability that such an event will cause a varying degree of loss. Resilience likewise refers to the organization of a biological, societal, or technological system such that it can withstand deleterious consequences of future risks. Although both risk and resilience pertain to the future, they are assessed by looking to the past – the past occurrence of harmful events, the losses incurred in these events, and the success or failure of systems to mitigate loss when these events occur. Most common risk and resilience measures rely on records extending a few decades into the past at most. However, much longer-term dynamics of risk and resilience are of equal if not greater importance for the sustainability of coupled socioecological systems which dominate our planet. Historical sciences, including archeology, are critical to assessing risk and resilience in deep time to plan for a sustainable future. The challenge is that both past and future are invisible; we can directly observe neither. We present examples from recent archeological research that provide insights into prehistoric risk and resilience to illustrate how archeology can meet this challenge through large-scale meta-analyses, data science, and modeling.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.006 | 0.026 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".