Uncertainty and perceived cause-effect help explain differences in adaptation responses between Swidden agriculture and agroforestry smallholders
Bibliographic record
Abstract
Swidden smallholders are among the most vulnerable groups to climate change. Many efforts have focused on incentivizing their transition to agroforestry, often with limited results. Such transitions, embedded in complex socio-environmental changes, generate uncertainties, often ignored in the science-policy interface. In this paper, we examine dispersed disciplinary developments in decision-making under uncertainty, apply the insights to a case study, and discuss results in the context of prevalent knowledge production assumptions and incentivized livelihood transitions policies. We use interview data from three communities in the Mexican Maya region to create aggregated mental models of smallholders who adopted agroforestry, and those who continue to practice traditional swidden agriculture. The mental models depict perceived causal connections—including uncertain or delayed—between hazards, causes, consequences and responses. Our results show substantial differences in mental models driven by length of explanatory pathways, attribution of hazards and portfolios of responses, suggesting that agroforesters were more prone to proactive behavior and/or more responsive to outside discourses. Agroforestry is effective in reducing some uncertainties in its bundled approach, but new uncertainties for which smallholders have no prior experience arise. Contrastingly, recurrent themes point to lower self-efficacy in swidden smallholders, which may help explain non-adoption. We caution that not recognizing differences in mental models among potential beneficiaries of incentivized interventions may inadvertently exacerbate inequalities, while unaddressed uncertainties may lead to future disadoption. As a scientific tool, mental model mapping can inform the design of adaptation measures by identifying new knowledge and conflicting rationales, and segmenting strategies for potential (non)adopters.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".