An Overview of the Horizons Foresight Method: Using the “Inner Game” of Foresight to Build System-Based Scenarios
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it