Bibliographic record
Abstract
Future visions embody our hopes and dreams, worries and fears about what is yet to come. While approaches such as speculative design, experiential futures and design fiction are more explicit about their commitment to exploring alternative futures, all design(ing) is oriented towards the future. However, increasing precarity, which is defined as a profound and objective uncertainty over the future, so that “there will be no future” becomes the dominant vision (Pulcini, 2020). While design is seen as giving form to futures (Mazé, 2016), design theorist Tony Fry cautions that designers have been complicit in what he terms defuturing, which means the negation of the world’s futures (2020). Then, in the face of increasing precarity, how can we re-orient towards designing hopeful futures instead of defuturing? Following Arjun Appadurai (2013), I join the call for reframing hope as a politics of action to make futures otherwise. Hope, in this sense, is not passive wishing or waiting but a factor in mobilising collective action for alternative futures. At the same time, fictional expectations about the future can be used to cement the present-day status quo. Thus, it matters what hopes we are designing with or whose hopes we are weaving into future visions. To begin exploring these questions, I examine future-oriented design practices and investigate how hope is embedded, materialised and enacted in future-making. I draw on data from an ethnographic field study of the design laboratory at the Silicon Valley Research and Development division of a multi-national technology company. In this presentation, I critically reflect on the role hope plays in future making and explore emerging design practices that highlight possibilities to employ hope for catalysing systemic change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.025 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.083 |
| Scholarly communication | 0.020 | 0.030 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".