Gendering women’s livelihoods in artisanal and small-scale mining: an introduction
Bibliographic record
Abstract
Until recently, women were largely invisible as miners within the array of initiatives, laws and policies seeking to regulate mining in sub-Saharan Africa. This invisibility is beginning to change as gender and women are increasingly referenced in mining reform initiatives. In this paper, we provide an overview of extant research on gender and women’s livelihoods in artisanal and small-scale mining (ASM), mostly of precious or high-value minerals, and some of the gaps that the papers assembled in this special issue address. This introductory paper also seeks to frame the special issue by questioning the forms of visibility of “women in mining” in policy and scholarly work. Knowledge claims about women and their mining work need to be emplaced within wider presumptions, power relations and political economies at various scales. Gender, we argue, provides an important analytical and methodological lens to critically consider the materialization of “women” in relation to ASM.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".