Extractives and Sustainable Community Development: A Comparative Study of Womenâs Livelihood Assets in the Americas
Bibliographic record
Abstract
The world has experienced a rapid growth in the mining industry due to increased demand for minerals. However, this situation has given rise to complexities in resource regions, compromising how women sustain their livelihoods. With increasing deregulation and globalization of the world economy, the livelihoods of women in resource-rich regions deserve special attention. Women in communities adjacent to extractive operations commonly experience a loss of livelihood options. Using case studies, this paper compares the livelihoods of women in two resource regions, Risaralda in Colombia and an Indigenous community in Nemiah Valley of British Columbia in Canada. This paper argues that the extractive industry should engage with women to enhance their assets and help them forge more sustainable livelihood options. The paper also makes recommendations to stakeholders on how livelihood assets can be enhanced to benefit women in resource development regions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.074 | 0.039 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".