An Overview of the Horizons Foresight Method: Using the “Inner Game” of Foresight to Build System-Based Scenarios
Bibliographic record
Abstract
Humans have an amazing capacity to imagine the future, and most foresight tools use this capacity but don’t explicitly support it. The Horizons Foresight Method puts this power to model and visualize at the center of the foresight process. This paper introduces foresight and scanning in general terms, describes how we can support the “inner game” of foresight, outlines the steps in the Horizons Foresight Method and some of the practical issues that arise when using it. There are many tools in the futurist’s toolbox and many good foresight methods. At Policy Horizons Canada, we use a variety of methods depending on the purpose of each foresight study. The Horizons Foresight Method is a strategic foresight method that was designed to help government policy analysts and decision-makers explore how complex systems could evolve and to address the kinds of policy relevant uncertainty these shifts generate. It provides a context for policy development and vision-building. All the tools integrated in the Horizons Foresight Method were developed in the field of futures studies. Teaching this method can expose students and practitioners to some of the most useful tools in doing foresight.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".