Understanding livelihood changes in the charcoal and baobab value chains during Covid-19 in rural Mozambique: The role of power, risk and civic-based stakeholder conventions
Bibliographic record
Abstract
Non-pharmaceutical interventions (NPIs) to reduce the transmission of Covid-19 had different repercussions for domestic, regional and global value chains, but empirical data are sparse on specific dynamics, particularly on their implications for value-chain stakeholders' local livelihoods. Through research including weekly phone interviews (n = 273 from May to July 2020) with panellists in six Mozambican communities, our research traced firstly how the baobab and charcoal value chains were affected by Covid NPIs, particularly in terms of producers' livelihoods. Secondly, we ask how our findings advance our understanding of the role of civic-based stakeholder conventions and different types of power in building viable local livelihoods. Our conceptual lens is based on a synthesis of value-chain and production-network analysis, convention theory and livelihood resilience focusing on power and risk. We found that Covid trading and transport restrictions considerably re-shaped value chains, albeit in different ways in each value chain. The global baobab value chain continued to provide earnings particularly to women, when other income sources were eliminated, with socially oriented stakeholders altering their operations to accommodate pandemic restrictions. By contrast, producers involved in the domestic, solely market-oriented charcoal value chain saw their selling opportunities and incomes reduced, with hunger rising in charcoal-dependent communities. Our paper argues that local livelihoods were more resilient under Covid NPIs if civic-based conventions and collective, social power were present.
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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 | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".