Land and livelihood in the age of COVID‐19: Implications for indigenous food producers in Ecuador
Bibliographic record
Abstract
Like many Latin American countries, Ecuador responded to COVID-19 by restricting trade and travel, a decision that disrupted the prevailing model of regional trade integration. Among some analysts, observations have been made that the lockdown represents a new opportunity to revitalize rural livelihoods and smallholder agriculture. This paper evaluates these claims by exploring the impact of COVID-19 on household food security and smallholder food production in Chimborazo, a highland province that is known for extremely high rates of poverty and the highest concentration of Kichwa-speaking Indigenous people in Ecuador. Drawing upon original empirical research, it makes the case that the prospects for revitalizing smallholder production remain structurally constrained by a legacy of land inequality and failed agrarian reform. According to our findings, the only sectors that thrived during the lockdown were ones that served local markets. For those requiring significant shipping and storage, merchants and traders were able to drive down farmgate prices, squeezing local producers. At the same time, new government legislation made it easier for employers to terminate wage labourers, undermining a vital source of income and employment for low-income households. Far from revitalizing smallholder agriculture, the pandemic appears to have further entrenched an economic model of supporting agribusiness at the expense of family farms and migrant labour.
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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 | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".