Ohallenges and opportunities when implementing strategic foresight: lessons learned when engaging stakeholders in climate-ecological research
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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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 | 0.362 | 0.357 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.025 | 0.048 |
| Scholarly communication | 0.037 | 0.065 |
| Open science | 0.006 | 0.036 |
| Research integrity | 0.014 | 0.027 |
| Insufficient payload (model declined to judge) | 0.007 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".