Deep-time perspectives on drylands: Archaeology as a lens for understanding long-term livelihood systems and resilience
Bibliographic record
Abstract
Abstract Drylands are still widely perceived as marginal areas, unsuitable for food production and long-term human settlement. This view, reinforced by mainstream global land use models, stands in sharp contrast with archaeological and ethnographic evidence showing that sustainable agriculture and pastoralism have long existed even in hyperarid regions. In this perspective article, we argue for the importance of applying archaeology to build a long-term narrative of land use management in drylands, highlighting the relevance of nonmechanized, resilient subsistence strategies as forms of biocultural heritage and sustainable alternatives rooted in indigenous priorities put in place over centuries. We contend that archaeology is key to shifting this narrative by documenting long-term socio-ecological adaptation in drylands. To this end, we present a range of archaeological methodologies that have helped trace techno-cultural developments in drylands, challenging persistent assumptions about the limits of human occupation and food production in arid environments.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.030 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".