Making Futures Present: A postcard from the future clears up your vision of the horizon
Bibliographic record
Abstract
Is what’s over the horizon a bit blurry for you? The future presents a greater feeling of certainty when you hold a piece of it in your hands. \n \nMaking Futures Present is a 30-minute exploration of an experiential foresight and design thinking method that helps to review and rethink the future by co-creating multiple scenarios. \n \nIt is very tempting for us to turn our gaze away from the future when it looks blurry, seems unknowable, and feels menacing. However, strategic foresight is a practice of low-risk thought experiments to reflect on the capacity to manage complex and turbulent scenarios. Making Futures Present helps us to envision preferred futures via design fiction objects. As a result, participants increase their comfort level with uncertainty and take steps in the present toward the future that they want. \n \nMaking Futures Present was awarded the Next Generation Foresight Practitioner award from the School of International Futures in 2018. It was also awarded the Most Significant Futures Work by the Association of Professional Futurists in 2019. It has been adapted for high school students, seniors, artists, writers, journalists, and people who are in career transitions. In 2020 it was adapted for youth anti-vaping research with the University of Toronto School of Public Health and recreated as an online research tool called Nod from 2050 in 2021. Most recently, it was adapted again for anti-vaping training onsite at a high school in Toronto. The facilitation instructions will be available free via a Creative Commons license soon.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.086 | 0.018 |
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".