Gendered governance and socio-economic differentiation among women artisanal and small-scale miners in Central and East Africa
Bibliographic record
Abstract
Drawing on qualitative research data from two gold artisanal and small-scale mining sites (ASGM), one in Democratic Republic of the Congo, the other in Uganda, this paper explores the authority arrangements that govern mining livelihoods in these sites, tracing their gendered forms and operation. The inter-relationship between these arrangements and women’s mining livelihoods is considered to further explore some of the socio-economic differentiation among women miners. In the context of increasing emphasis on formalizing the ASM sector in Sub-Saharan Africa, including through licenses and formation of associations and cooperatives, both the gendered organization of mine site governance and social differential among women miners have important implications. Formalization efforts in the ASM sector are rightly critiqued for failing to account for social differentiation that may allow elites to control licenses and associations. But also important, our research suggests, is the gendered inequalities that characterize existing authority arrangements, and the differentiation among women that may allow some women to organize and not others.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".