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Record W4290802185 · doi:10.1215/9781478023074-006

Mineral Mapping and the Global Cold War in Sénégal Oriental

2022· book-chapter· en· W4290802185 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsnot available
Fundersnot available
KeywordsCold warMineralGeographyHistoryGeologyGeochemistryPolitical scienceMetallurgyMaterials scienceLaw

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.250
Threshold uncertainty score0.497

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.007
GPT teacher head0.170
Teacher spread0.162 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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