Futuring: Toward a more inclusive and empathetic approach
Bibliographic record
Abstract
In a world marked by rapid and constant change, societies are grappling with transitions like never before. Designers are often at the forefront of this change, navigating ethical implications and confronting uncomfortable truths while interacting with individuals holding differing opinions to drive meaningful change. This article delves into the designer’s responsibility to be aware of their character and position in “design defuturing,” meaning their influence on negating certain futures. The authors provide experiences of narrowing the scope in design projects through speculative scenarios and reflect on the implications of this practice. The article also offers the potential of mindfulness and Buddhist frameworks and ideas to guide designers toward creating futures and producing design fictions that are egoless, detached from outcomes, and recognise impermanence and the interdependence of all beings. These practices help designers to enact “contra-innovation” and challenge the dominant narratives and actions of innovation that defuture. The concept of non-self challenges designers to go beyond their individual perspectives and ego-driven design approaches. It helps designers to step outside of themselves and better connect with the people and planet they are designing with and for. This encourages a more inclusive, adaptable, and empathetic approach that aligns with the principles of human-centred and sustainable design.
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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 | 0.034 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.016 | 0.053 |
| Scholarly communication | 0.030 | 0.029 |
| Open science | 0.006 | 0.028 |
| Research integrity | 0.013 | 0.014 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".