Enacting Maasai and Palaeoanthropological Versions of Drought in Oldupai Gorge, Tanzania
Bibliographic record
Abstract
While palaeoanthropologists have travelled to Tanzania’s renowned human origins site of Oldupai Gorge for over a century, lasting collaboration has yet to be established with the Maasai pastoralists who inhabit the area. This paper uses actor-network-theory and the concept of enactment to compare palaeoanthropological and Maasai livelihoods and to explore why collaboration has been infrequent. Here we show that both groups’ subsistence strategies had to effectively navigate large political-economic contexts. To support their respective livelihoods, scientists and locals expertly acquired resources in non-scientific and non-pastoral worlds. Both Maasai peoples and researchers created and multiplied reality and ontologies by enacting composite – yet conflicting – versions of hybrid drought. The exigencies associated with palaeoanthropological and Maasai subsistence have hindered meaningful collaboration between the groups, despite the fact that members of both dug in the Gorge to address drought. While the legitimisation of scientific ontologies is ultimately well-intentioned, Maasai drought unfortunately remains unaddressed.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".