Foresight and futures thinking for international development co‐operation: Promises and pitfalls
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
Abstract Motivation Strategic foresight is gaining traction for anticipating changes in a volatile, uncertain, complex, and ambiguous (VUCA) world—one which will require different mindsets and approaches. Yet international development co‐operation practitioners have been slow to adopt foresight. Purpose What promises and pitfalls should development practitioners consider in order to integrate strategic foresight into their work? Methods and approach We review the literature on strategic foresight applied to development. We draw on reflections from the articles included in this special issue. We incorporate the International Development Research Centre's experiences and early insights on the use of foresight for development. Findings Strategic foresight provides tools to anticipate long‐term and potentially disruptive change. To apply the approach effectively, organizations need to understand the debates about foresight. But no one size fits all: organizations must identify where and how foresight can best be used; be clear on its purpose, use, and end‐users; be sensitive to how foresight intersects with broader calls for decolonizing development and the future; and should adapt methods to different sociocultural contexts. Connecting foresight practitioners and international development actors to explore potential synergies between these two worlds offers opportunities to innovate. Policy implications Traditional, short‐term strategic planning, and reactive responses to emerging crises, are increasingly ill‐suited to a VUCA world. To be fit for the future, international development actors must consider adding proactive longer‐term anticipatory planning—that accommodates more systematic understanding and appreciation of plausible futures—to reactive responses.
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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 | 0.003 | 0.001 |
| 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 | 0.001 | 0.000 |
| 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