Can scenario planning catalyse transformational change? Evaluating a climate change policy case study in Mali
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The potential of participatory scenario processes to catalyse individual and collective transformation \nand policy change is emphasised in several theoretical reflections. Participatory scenario \nprocesses are believed to enhance participants’ systems understanding, learning, networking and \nsubsequent changes in practices. However, limited empirical evidence is available to prove these \nassumptions. This study aimed to contribute to this knowledge gap. It evaluates whether these \noutcomes had resulted from the scenario planning exercise and the extent to which they can \ncontribute to transformational processes. The research focused on a district level case study in \nrural Mali which examined food security and necessary policy changes in the context of climate \nchange. The analyses of interviews with 26 participants carried out 12 months after the workshop \nsuggested positive changes in learning and networking, but only limited influence on systems \nunderstanding. There was limited change in practice, but the reported changes occurred at the \nindividual level, and no policy outcomes were evident. However, by building the adaptive capacity \nof participants, the scenario process had laid the foundation for ongoing collective action, \nand potential institutional and policy transformation. We conclude that to enhance the resilience \nof agricultural and food systems under climate change, participatory scenario processes require a \nbroader range of cross-scale actors’ engagement to support transformational changes. Such \nprocess will both catalyse deeper learning and more effective link with national level policymaking \nprocess. In addition, individual scenario planning exercises are unlikely to generate \nsufficient learning and reflection, and instead they should form one component of more extensive \nand deliberate stakeholder engagement, learning and evaluation processes.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it