A New Paradigm: The African Early Iron Age without Bantu Migrations
Bibliographic record
Abstract
Between 1000 BC and AD 1000, or so the story goes, sub-Saharan Africa was the setting for one of the all-time great population movements of antiquity—the Bantu migrations. Sweeping to and fro across the continent in a kind of grand migrationary gavotte, absorbing or brushing aside the autochthonous hunter-gatherers, the ancestral Bantu speakers carried with them on their march the seeds of a settled life fueled by food production and iron technology. Their movements are represented by large arrows scything across big blank maps of the African interior. How good is the evidence that any of it ever happened? In this paper we shall examine some of the serious methodological and practical problems that bedevil the migrationary model. We shall also present an alternative model for the prehistory of sub-Saharan Africa: in brief, that the development of the Early Iron Age in Africa was a process rather than an event; that autochthonous populations gradually adopted the suite of traits that define the Early Iron Age, without any large-scale movement of peoples; and that increasing sedentarization actually led to a population decline which was only overcome after AD 500. The model constitutes a new paradigm that emphasizes continuity and takes into account a few observations that are awkward for the migrationary paradigm: that sub-Saharan Africa has a difficult topography that may put certain constraints on population movements, and that the continent was slowly filling up on its own when events starting in the sixteenth century turned the autochthonous peoples' lives upside down.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.033 |
| Scholarly communication | 0.007 | 0.028 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".