Zulu pottery production in the Lower Thukela Basin, KwaZulu-Natal, South Africa
Bibliographic record
Abstract
The organization of modern and historically recent pottery production in southern Africa is an understudied subject. This situation limits the ability of social scientists to investigate the historical forces that have shaped African pottery production systems and provide appropriate data and interpretive frameworks for inferring the organization of ancient production systems. This paper summarizes fieldwork conducted over the past decade with Zulu potters in the Lower Thukela Basin of KwaZulu-Natal, South Africa, with the intention of examining the socially influenced decisions affecting ceramic production. These data allow us for the first time to characterise pottery production by Nguni-speaking peoples in southern Africa in terms of production technology, the scale of production and distribution, seasonality and labour organization, and learning strategies (apprenticeship). The pottery production model resulting from research in the Thukela Basin is an attempt to explain why and how the social networks that potters are involved in impact the visible and technical dimensions of pottery in a modern southern African context. This model has implications for archaeological research within the region because it complements current archaeological approaches to pottery style in southern Africa while providing a much finer degree of sensitivity to pottery variability than is otherwise available. To cite this article: Fowler, K.D. Zulu pottery production in the Lower Thukela Basin, KwaZulu-Natal, South Africa. Southern African Humanities 20 : 477-511.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".