Women and Mine Development: Capturing Vulnerability Using Open Data and GIS
Bibliographic record
Abstract
Aboriginal and rural women are among the most vulnerable members of Canadian society and are particularly vulnerable to health impacts associated with the extractive industry (EI) such as increased substance abuse, domestic and community violence and prostitution. Yet, these impacts remain largely unidentified and unmitigated in Canadian federal and provincial EIAs. In British Columbia (BC), EI project proponents, impact assessors, reviewers and communities themselves have open access to robust digital health and spatial datasets. These datasets can be used to identify areas and populations vulnerable to impacts during the EIA process, as well as track conditions over time to evaluate the effectiveness of mitigation measures. In 2010, community members in north-central BC approached the research team to help them better understand and prepare for health impacts associated with a new open-pit 110,000 tonne/day Copper-Gold Mine. Community members expressed dissatisfaction with how the EIA process captured community health issues such as loss of land, increased traffic, in-migration, impacts on health and social services, crime and violence and the vulnerability of women and youth. As part of this research project, this work seeks to improve how the EIA process captures and addresses impacts to women living in remote communities in BC. This presentation proposes a tool for better capturing the vulnerability of rural and Aboriginal women living in remote areas to violence and violent victimization associated with extractive projects. This web-based, GIS tool will integrate existing health and spatial datasets to identify areas of BC where violence is endemic and women may be vulnerable in effort for communities, proponents, and government to better plan and implement mitigation measures that address violence. This research is part of the Extractive Industry and Community Health Project funded by the Canadian Institutes for Health Research.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".