Resilience of Past Landscapes: Resilience Theory, Society, and the Longue Durée
Bibliographic record
Abstract
Resilience theory is an expanding body of ideas that attempts to provide explanations for the source and role of change in adaptive systems, particularly the kinds of change that are transforming. Scholars from various disciplines have contributed to the current state of this formulation. This article proposes that resilience theory would benefit from an increasing collaboration with archaeologists, who would provide a long-term perspective on adaptive cycles. Although archaeologists and anthropologists have written provocatively about studying the resilience of past and present societies, such an approach has not become common in these disciplines. We suggest, however, that a resilience framework offers a potential mechanism for reinvigorating the conceptual base of archaeological and anthropological disciplines. To make this case, we (1) highlight three features of resilience theory, including cross-scale interactions, information flow, and phases of the adaptive cycle; (2) examine the extent to which purely natural or social science analyses would give complementary or contradictory conclusions; and (3) discuss the implications of using a long-term integrative perspective for understanding linked social and ecological systems.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.012 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.003 |
| 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".