Can scenario planning catalyse transformational change? Evaluating a climate change policy case study in Mali
Bibliographic record
Abstract
The potential of participatory scenario processes to catalyse individual and collective transformation and policy change is emphasised in several theoretical reflections. Participatory scenario processes are believed to enhance participants’ systems understanding, learning, networking and subsequent changes in practices. However, limited empirical evidence is available to prove these assumptions. This study aimed to contribute to this knowledge gap. It evaluates whether these outcomes had resulted from the scenario planning exercise and the extent to which they can contribute to transformational processes. The research focused on a district level case study in rural Mali which examined food security and necessary policy changes in the context of climate change. The analyses of interviews with 26 participants carried out 12 months after the workshop suggested positive changes in learning and networking, but only limited influence on systems understanding. There was limited change in practice, but the reported changes occurred at the individual level, and no policy outcomes were evident. However, by building the adaptive capacity of participants, the scenario process had laid the foundation for ongoing collective action, and potential institutional and policy transformation. We conclude that to enhance the resilience of agricultural and food systems under climate change, participatory scenario processes require a broader range of cross-scale actors’ engagement to support transformational changes. Such process will both catalyse deeper learning and more effective link with national level policy-making process. In addition, individual scenario planning exercises are unlikely to generate sufficient learning and reflection, and instead they should form one component of more extensive and deliberate stakeholder engagement, learning and evaluation processes.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".