Science and technology foresight baker's dozen: a pocket primer of comparative and combined foresight methods
Bibliographic record
Abstract
Purpose This paper aims to fill a perceived gap in the tool bag of a foresight practitioner – namely the need for a quickly accessible and concise overview of the main methods being employed in contemporary foresight, and some guidance on when and how one can select or combine several methods within a single project or focus area to achieve the best results. The intention is that such a primer can be easily reproduced into a format suitable and portable for managers to consult when in project meetings to design foresight processes and select methods. Design/methodology/approach Using a matrix table plus some text analysis and diagrams, a concise review of the dominant and most innovative tools for framing technology foresight processes is developed and summarized. Findings The paper produces 13 foresight methods classified, summarized and referenced with a limited selection of literature references. Practical implications The intention is that the main table and diagrams can be easily copied to a standard poster or made into a pocket pamphlet for those who wish to carry the primer with them. Originality/value The insights in the paper are derived from the authors' foresight design and management experience, from inputs and discussions and from relevant literature sources. It is envisioned that this will be only the first pocket primer, with further editions expected in the future, as more diverse experience is gained with these methods and new approaches are tried and tested.
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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 | 0.069 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.020 | 0.015 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.016 | 0.029 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 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".