Scaffolding Futures Making: Facilitating a More Democratizing Futuring Practice
Bibliographic record
Abstract
In this article, we develop a conceptual framework for democratizing futures making, a practice often dominated by corporate and professional elites. We argue that more inclusive participation requires a deeper engagement with the pedagogical dimensions of how people come to imagine, explore, and shape the future together. To this end, we introduce scaffolding as a structured approach to futures making through embedded, embodied, and emergent processes. In the first section, we critically examine futures literacy as an approach to democratizing futures making and show why its cognitivist and linguistic focus is limited. Drawing on design theory and organizational theory, we outline how a new materialist perspective offers a more viable alternative. In the second section, we revisit scaffolding as a pedagogical metaphor used to organize learning, reframing it through materialist perspectives to avoid prescriptive interpretations. In the final section, we describe how scaffolding can be put into practice as part of futures making. • A clearer conceptualization of how pedagogy can support the democratization of futures making. • Although futures literacy offers useful insights for teaching futures making, it relies too heavily on cognitive and linguistic models that overlook the material and grounded aspects of collective aspiration. • The traditional pedagogical metaphor of scaffolding often emphasizes instructor control and learner passivity, despite its learner-centered origins. • Drawing on new materialist interpretations, the article advances a socio-material understanding of scaffolding in which futures making involves learning that is embedded, embodied, and emergent.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.022 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.003 |
| 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".