How can climate change and its interaction with other compounding risks be considered in evaluation? Experiences from Vietnam
Bibliographic record
Abstract
While evaluations play a critical role in accounting for and learning from context, it is unclear how evaluations can take account of climate change. Our objective was to explore how climate change and its interaction with other contextual factors influenced One Health food safety programs. To do so, we integrated questions about climate change into a qualitative evaluation study of an ongoing, multi-sectoral program aiming to improve pork safety in Vietnam called SafePORK. We conducted remote interviews with program researchers (<i>n</i> = 7) and program participants (<i>n</i> = 23). Based on our analysis, researchers believed climate change had potential impacts on the program but noted evidence was lacking, while program participants (slaughterhouse workers and retailers) shared how they were experiencing and adapting to the impacts of climate change. Climate change also interacted with other contextual factors to introduce additional complexities. Our study underscored the importance of assessing climate factors in evaluation and building adaptive capacity in programming.
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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 | 0.000 |
| 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) | 0.014 | 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".