Mineral Mapping and the Global Cold War in Sénégal Oriental
Bibliographic record
Abstract
When "the Rus sians" arrived in Kédougou in 1971, they requested El Hadj Mori Tigana's compound by name.In the early 1960s, just after Senegal's in de pen dence from France, Mori Tigana earned a reputation as a skilled prospecting aid in working with earth scientists from Switzerland, Poland, Canada, the Soviet Union, and from the Senegalese capital, Dakar.Tigana assisted Soviet scientists in establishing a base camp in Mamakono, home of the Atlantic-era "slave king, " Taubry Sidibé. 1 In the 1970s, Mamakono's population hovered at several hundred.The village had already hosted four international mineral research missions, the products of cooperative research protocols signed between in de pen dent Senegal and the United Nations (un), France, and the Soviet Union.The 1960s was "the time of electricity in Mamakono, " when geologists powered floodlights with diesel generators to sort soil samples at night and children played in the hazy glow of artificial light.Senegalese ministers visited elders in the village, urging them to support the work of mineral missions.Geological research, they were told, would transform Kédougou into an engine of industrial development for in de pen dent Senegal.The mineral missions did document major reserves of iron and gold.But prices for hard metals were low at the time, and Kédougou was distant from Dakar's ports.As a result, mineral prospects discovered by these missions became "shelf proj ects"-shelved in the archives of Senegal's Direction des Mines et de la Géologie (Department of Mines and Geology [dmg]). 2 De cades later, in the early 2000s, this geological research proved crucial to Senegal's emergence as a major gold producer.Many of the corporate-owned gold mines operating today in Senegal, Mali, and Guinea began as independence-era shelf proj ects.This chapter shifts the geographic and temporal focus of this book from the goldfields of the aof to the Birimian rocks of a single postcolonial state: 5 Mineral Mapping and the Global Cold War in Sénégal Oriental
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".