What We Speculate About When We Speculate About Sustainable HCI
Bibliographic record
Abstract
Fears of climate change and the escalating impacts of environmental damage are growing, and recent papers in the area of Sustainable HCI have called for urgent, non-linear solutions to these problems. Speculative design, along with related approaches including design fiction, have been taken up as means of navigating the "wicked problems" that structure contemporary nature/society relations. We conduct a survey of speculative design papers published in ACM venues between 2008 and 2021, assessing fundamental questions such as who is involved in the process, how is sustainability framed, and how is speculation used. Our evaluation of this body of work yielded mixed results; we find both promising trends as well as notable and problematic limitations in how the HCI community is taking up speculative practice in this domain. We build upon this evaluation to offer four provocations to designers seeking to use speculative practice in support of sustainability goals.
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 | 0.036 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.011 | 0.050 |
| Scholarly communication | 0.025 | 0.044 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".