Buffering new risks? Environmental, social and economic changes in the Turkana Basin during and after the African Humid Period
Bibliographic record
Abstract
This paper evaluates risk-oriented frameworks for explaining environmental, social, and economic changes faced by fishing and herding communities in the Turkana Basin during and after the African Humid Period (AHP, 15–5 ka). The orbitally-forced AHP created moist conditions, high lake levels, and unusual hydrological connections across much of northern and eastern Africa. As arid conditions set in and rainfall decreased between 5.3 and 3.9 ka in eastern Africa, Lake Turkana (NW Kenya) shrank dramatically. Shoreline retreat coincided with an expansion of open plains, creating new ecological conditions and potential opportunities for early herders in the basin. In this changing landscape, economies shifted from food procurement (fishing/hunting aquatic resources) to food production (herding), likely through both in-migration by pastoralists and adoption of herding by local fishers. Early pastoralists also built at least seven megalithic pillar sites that served as communal cemeteries during this time. Recent research has shown that local environmental dynamics – both during and after the AHP – were complex, demanding a more careful interrogation of the notion that post-AHP life entailed new and/or heightened risks. Risk-buffering strategies might include mobility, diversification, physical storage, and exchange. Archaeologists working around Lake Turkana have proposed that economic shifts from fishing to pastoralism entailed increased mobility as a risk-buffering strategy to deal with aridity and resource unpredictability, and that pillar sites – as fixed landmarks in an unstable landscape – provided settings for congregation and exchange amongst increasingly mobile herding communities. However, recent research has shown that local environmental dynamics in the Lake Turkana basin – both during and after the AHP – were more complex than previously thought, necessitating re-evaluation of the notion that post-AHP life entailed new and/or heightened risks. Here, we explore risk buffering strategies (e.g. mobility, diversification, physical storage and/or exchange) as only one category of potential explanation for the new social practices observed in the region at this time. Gauging their applicability requires us to (a) assess the spatial mobility of communities and individuals interred at pillar sites; (b) evaluate whether and how mobility strategies may have changed as pastoralism supplanted fishing; and (c) examine alternative explanations for social and economic changes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".