Scientific facts and oral traditions in Oldupai Gorge, Tanzania: Symmetrically analysing palaeoanthropological and Maasai black boxes
Bibliographic record
Abstract
Reminiscent of past colonial practices, contemporary archaeological research in Africa is still often governed and carried out by foreign entities that move Africans aside from their pasts and their countries’ archaeological heritage. Oldupai Gorge, located in Tanzania’s Ngorongoro Conservation Area, is a flagship human evolution research site. Less recognized is that a Maasai pastoral society inhabits the region. Despite over a century of excavations in the ‘birthplace of humanity’, local Maasai and palaeoanthropologists have rarely affiliated with each other. Furthermore, a lingering and erroneous characterization of the Maasai as archaic, environmentally damaging and premodern continues to guide policies that compromise Maasai pastoral livelihoods. This article utilizes actor–network theory to ethnographically and symmetrically compare the epistemic cultures of both the Maasai and palaeoanthropologists in Oldupai, and argues that while scientific and Maasai knowledge may differ in cultural content, both groups built ‘black boxes’ – such as scientific facts and oral traditions – in parallel and equally logical forms. Since there are no fundamental cognitive differences between members of each group, there are no justifiable reasons that the Maasai should continue to be excluded from research in their homeland and the myriad benefits that it can bring.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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 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".