Bibliographic record
Abstract
Purpose The aim of this article and special issue is to propose a framework for foresight impacts on policy and decision making. The need to identify direct impacts, measure them and identify the factors that lead to impact is the primary objective of the special issue and, as outlined in the article, represents a critical addition to the foresight field. On the basis of case studies, experience, and theoretical‐evaluative frameworks this issue seels to offer suggestions regarding the factors that may help policy makers, academics, consultants, and others involved in foresight produce impactful results. Design/methodology/approach The methodology deployed for this article is both empirical and meta analysis. This introductory article is based on the special issue articles as well as the authors' extensive practical experiences in foresight. Findings Foresight does impact policy. Case studies and experiences in Europe, North America, Africa and Asia identified in the special issue provide support for this. Also, as difficult as it is to measure impact, the authors explore several frameworks that will help the foresight community demonstrate impact and prove the value of foresight. Originality/value The article highlights several frameworks that will help the foresight community demonstrate impact and prove the value of foresight.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".