Tracing gendered and classed dimension of formalization of artisanal and small-scale mining efforts in Mozambique
Bibliographic record
Abstract
Many formalization of artisanal and small-scale mining (ASM) policies in Africa emphasize increasing tenure security through mining titles and the mandatory creation of cooperatives. These are promoted as a means of poverty-alleviation, reducing environmental harms, and ensuring community benefits, which could include the empowerment of women. Drawing from research conducted in gold ASM areas in Manica, Mozambique together with analyses of transnational law and policy on miners’ cooperatives and ASM formalization interventions, this paper examines how these efforts have expressed significant gendered and class inequities. It analyses how the authority and control rights of the associations/cooperatives privilege men who are local political or economic leaders, which in one case was widely celebrated as an early and leading example of the benefits of formalization. The result, we find, was reduced access to gold mining livelihoods for women. Our analysis underscores the importance of examining who actually receives control rights in formalization efforts and how these are gendered and classed in practice, rather than assuming the declared collective benefits such as gender empowerment will emerge from them. • Women face many obstacles in their livelihood activities in artisanal gold mining. • Efforts to formalize artisanal and small-scale mining (ASM) in Africa often hinge on property rights and the establishment of cooperatives. • ASM formalization efforts in Mozambique have distinct classed and gendered inequalities. • Women miners in Manica district, Mozambique have largely not benefited from formalization efforts.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".