Changes in household wealth in communities living in proximity to a large-scale copper mine in Zambia
Bibliographic record
Abstract
Large-scale mining can alter the living conditions of surrounding communities in positive and negative ways. A health impact assessment conducted in the context of a newly developed large-scale copper mine in rural Zambia gave us the opportunity to measure changes in health determinants over time. We conducted periodic household surveys at baseline in 2011, during the construction phase in 2015 and during the operational phase in 2019. Data collected included economic indicators that were based on the standardized list of household assets used in the Zambia Demographic and Health Survey, which we subsequently converted into a wealth score using principal component analysis. We compared mean wealth scores in six communities directly impacted by the mine with comparison communities, as well as the rest of the North-Western province of Zambia. A difference-in-differences linear regression model was used to compare changes over time. Mean wealth of the communities near the mine was significantly lower at baseline than that of the North-Western province (−0.54 points; p-value<0.001) in 2011, but surpassed the regional average in 2019 (+1.07 points; p-value <0.001). Mean wealth increased more rapidly in communities directly impacted by mine than in the comparison communities (+0.30 points, p-value <0.001). These results suggest a positive impact on living conditions in communities living near this copper mine. Our findings underscore the potential of the mining sector to contribute to economic development in Zambia.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".