Adaptation Strategies Through Mining Compensation in Mabayi Commune, Burundi
Bibliographic record
Abstract
One of main effects of mining on rural agriculture is the loss of farmland by households living near mining sites. In return, these households should receive compensation. This compensation, if well invested, may lead to improved livelihoods for these farming households. If not, these households may find their livelihoods deteriorating if the compensation is not properly managed. This paper aims to analyze household compensation investment strategies in Mabayi commune (Burundi) and their effects on the livelihoods of households affected by mining activities. A survey of 140 households, interviews with key informants, and observations were conducted in July and August 2022 on Gahoma and Ruhororo hills where foreign company ‘‘Tanganyika Mining Burundi (TMB)’’ and local cooperative ‘‘Dukorere Hamwe Dusoze Ikivi (DHDI)’’ were carrying out their activities respectively since December 2018, in Mabayi commune. Results showed that 17 out of 20 households (85%) and 13 out of 17 households (76.5%) had invested their compensation well, in Gahoma and Ruhororo hills respectively. They had improved or maintained their overall quantity of agricultural production, and improved their livelihoods in general. On other hand, 3 households (15%) and 4 households (23.5%) had invested their compensation inappropriately, in Gahoma and Ruhororo respectively. They had suffered reduction in their overall quantity of agricultural production, and experienced deterioration of their livelihoods in general. Fair and up-front compensation for households, assistance in how to invest compensation, capacity-building in agriculture and alternative activities, should maximize opportunities for improved livelihoods. The mining company and cooperative must also comply with environmental regulations.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".