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Women and Mining in Africa

2023· reference-entry· en· W4389894571 on OpenAlexaff
Blair Rutherford, Doris Buss

Bibliographic record

VenueOxford Research Encyclopedia of African History · 2023
Typereference-entry
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsCarleton University
Fundersnot available
KeywordsColonialismColonial ruleMining industryLivelihoodGold miningCONQUESTLegislationPolitical scienceGeographyEthnologyHistoryLawEngineeringArchaeologyAncient historyAgriculture

Abstract

fetched live from OpenAlex

Abstract Women have long participated in mining in Africa and have been implicated in it in varied ways. The combination of archaeological research, oral histories, comparisons with colonial and postcolonial mining activities, and a few written observations show that African women were active in various mining activities throughout the continent before Europeans began their formal colonization of much of the continent. Conquest by different European states and colonial rule brought not only a new type of mining and new players, networks, and markets into many parts of Africa but also new gendered norms and discourses when it came to women working in mines. Colonial legislation often prohibited women from working underground, and women’s work in mines was actively discouraged or hidden. European-controlled industrial mining in Africa in the late 19th century up until the 1970s was labor intensive, almost exclusively hiring men, most of whom were African. Yet these industrial mining areas attracted many women for economic and social reasons. These women became the targets of varied types of moral projects from different colonial and African authorities, which strongly shaped the pathways, possibilities, and barriers to varied (non-mining) economic activities for African women in the mining communities. The end of colonial rule and the emerging independent governments across Africa starting in the 1950s saw significant changes for women and mining in different parts of the continent, even though there are many strong continuities from the colonial period. These continuities and changes are apparent when examining access to mining livelihoods and working conditions for women and the role of family dynamics, both in terms of industrial mining and artisanal and small-scale mining. There is also a growing targeting of women and mining in Africa in policies, programs, and by social movements.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.456
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.055
GPT teacher head0.263
Teacher spread0.208 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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
Published2023
Admission routes1
Has abstractyes

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