Measuring impacts of transformative landscape approaches to agroecology: lessons from Laos. [ID278]
Bibliographic record
Abstract
In the northern uplands of Laos, landscape mosaics and people livelihoods rely on complex interactions, preventing the straightforward adoption of sustainable land management techniques despite their demonstrated performances as compared to (i) swidden systems with shortening fallow periods or (ii) monocropping systems based on the use of chemical inputs and/or mechanical tillage. To facilitate the dissemination of agroecology innovations in remote upland villages, the Eco-Friendly Intensification and Climate resilient Agricultural Systems (EFICAS) project is engaging with village communities into landscape level transformations of agricultural production and resource management. Since 2014, the project staff works closely with local communities on a theory of change process that promotes agroecology practices such as conservation agriculture, agroforestry, system of rice intensification, or integrated farming. Local stakeholders envision their desirable village landscape through participatory land use planning and then engage into successive learning loops to co-produce their own development pathways towards the collectively agreed land use plan. An impact monitoring systems has been setup since the beginning of the project to demonstrate the effectiveness of transformative landscape approaches on achieving sustainable development goals, including climate change mitigation and adaptation. We selected twelve pairs of similar villages covering the large diversity of agroecological and socioeconomic contexts found in the study region. Interventions were organized in one village of each pair while the other village was used as control. We co-produced the monitoring indicators with local communities to make sure they were meaningful to them and actionable to adjust the interventions all along the transformative process. The participatory monitoring system consisted in three successive rounds of data collection organized in 2014 (baseline), 2016 and 2018 in both intervention and control villages. We co-designed and then used the EFICAS role-play game to explore with farmers scenarios of changes and monitor social learning along the transformative pathway.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".