“Against All Odds”. Female Small Scale Mine Owners in Gwanda, Zimbabwe
Bibliographic record
Abstract
Historically, the mining sector has been a preserve of males, making it a highly male dominated environment which had very few women. Even in contemporary periods, the mining sector is still largely viewed as a gender “blind” sector to a larger extent. The study sought to explore the challenges faced by female small scale mine owners and how they have managed to survive in the harsh mining environment in which they operate. Study results indicate female mine owners face daunting challenges such as lack of financial capital and high costs associated with mining activities, lack of equipment, lack of technical knowledge of mining, as well as legal and policy constraints. Regardless of these challenges these mining start-ups by women have managed to survive and even grow in the harsh economic and political environment in Zimbabwe. The study concluded that challenges faced by female mine owners can be traced to gender disparity whose genesis is the patriarchal nature of Zimbabwean society and the untenable economic and political climate that has been in existence in Zimbabwe since the year 2000. Given a conducive socio-political and economic environment as well as a permitting legal and policy framework, women entrepreneurs can play a significant role in the economic transformation of the country.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".