FORESIGHT FRONTLINES: GLOBAL INSIGHTS ON STRATEGIC FORESIGHT INPOLICY MAKING
Bibliographic record
Abstract
In an era marked by volatility, uncertainty, complexity, and ambiguity (VUCA) (Bennis and Nanus, 1985), governments worldwide are increasingly turning to strategic foresight to anticipate and shape future challenges and opportunities. This paper presents a secondary research analysis of the institutionalization and application of strategic foresight in policy-making across diverse geopolitical contexts. By examining governmental foresight frameworks, national foresight programs, and policy innovation labs from countries including Finland (Heo and Seo, 2021) (OECD, 2022), Singapore (CSF, n/a), Canada (Government of Canada, 2022), and the European Union (European Commission, 2020), the study identifies common patterns, enabling conditions, and obstacles in embedding foresight into governance structures. The research also explores the varied cultural, political, and administrative lenses through which foresight is interpreted and operationalized, offering a comparative perspective on its effectiveness and impact. Ultimately, the paper proposes a typology of foresight adoption in policy-making and offers reflections on how global practices can inform more anticipatory and resilient governance. This contribution aims to enrich the discourse on strategic foresight as a critical competency for future-ready policy development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.013 | 0.026 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| 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 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".