Bucking the trend: Population resilience in a marginal environment
Bibliographic record
Abstract
Evaluating the impact of environmental changes on past societies is frequently confounded by the difficulty of establishing cause-and-effect at relevant scales of analysis. Commonly, paleoenvironmental records lack the temporal and spatial resolution to link them with historic events, yet there remains a tendency to correlate climate change and cultural transformations on the basis of their seeming synchronicity. Here, we challenge perceptions of societal vulnerability to past environmental change using an integrated paleoenvironmental and land-use history of a remote upland site in the north of Ireland. We present a high-resolution, multi-proxy record that illustrates extended occupation of this marginal locality throughout the climate oscillations of the last millennium. Importantly, historically-dated volcanic ash markers enable us to pinpoint precisely in our record the timing of major national demographic crises such as the Black Death and the European, Irish and Great (Potato) Famines. We find no evidence that climate downturns or demographic collapses had an enduring impact on the use of the uplands: either the community escaped the effects of these events, or population levels recovered rapidly enough (within a generation) to leave no appreciable mark on the palaeoenvironmental record. Our findings serve to illustrate the spatial complexity of human activity that can enable communities to withstand or quickly bounce back from largescale calamities. In neglecting to consider such local-scale variability in social and economic organization, generalized models of societal collapse risk overplaying the vulnerability of populations to long- and short-term ecological stressors to the detriment of identifying the social constraints that influence a population's response to change.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| 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".