Ohallenges and opportunities when implementing strategic foresight: lessons learned when engaging stakeholders in climate-ecological research
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
Ecosystems are currently experiencing rapid changes. Decision-makers need to anticipate future changes or challenges that will emerge in order to implement both short-term actions and long-term strategies for reducing undesirable impacts. Strategic foresight has been proposed to help resolve these challenges for better planning and decision-making in an uncertain future. This structured process scrutinizes the options in an uncertain future. By exploring multiple possible futures, this process can offer insights into the nature of potential changes, and thereby to better anticipate future changes and their impacts. This process is performed in close partnership with multiple actors in order to collect broader perspectives about potential futures. Through a large research initiative, we applied the strategic foresight protocol to a set of different case studies, allowing us as academic ecologists to reflect on the circumstances that may be influential for the success of this approach. Here, we present what worked and what did not, along with our perception of the underlying reasons. We highlight that the success of such an endeavour depends on the willingness of the people involved, and that building social capital among all participants involved directly from the start is essential for building the trust needed to ensure an effective functioning among social groups with different interests and values.
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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 | 0.016 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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