Foresight and futures thinking for international development co‐operation: Promises and pitfalls
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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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 | 0.047 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.008 | 0.042 |
| Scholarly communication | 0.023 | 0.040 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".