Imagining Sustainable Futures: Expanding the Discussion on Sustainable HCI
Bibliographic record
Abstract
Futures" [1] to map the various perspectives from which the CHI community currently addresses the problem of climate change.By bringing together these different perspectives, our intent was to find contact points among them and create synergies to imagine sustainable futures together.The workshop was met with great interest, highlighting the need for discussion spaces on climate change in the CHI community.We received 46 submissions (40 of which were accepted) and welcomed 53 participants (16 online and 37 in person in Hamburg, Germany).For the past 15 years, in light of biodiversity loss, ocean acidification, droughts, floods, and threats to humans' and nonhumans' health, life, and activities, HCI researchers have been reflecting on the role their work can play in reducing the impact of climate change.Recently, the discourse on climate change in the HCI community has expanded to include effective communication to raise citizens' awareness, policy design, the value of biodiversity, and the perspectives of nonhuman actors.During CHI 2023, we organized the workshop "HCI for Climate Change: Imagining Sustainable F Insights → HCI researchers should work with other disciplines and include nonhuman perspectives to develop a systemic understanding of climate change.→ A cultural shift from the concepts of persuasion, personhood, and property toward collectively nurtured common goods is needed to trigger collective action.→ Hopeful visions of the future might help to contrast ecoanxiety and denialism.
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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 | 0.042 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.013 | 0.032 |
| Scholarly communication | 0.029 | 0.069 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.018 | 0.020 |
| Insufficient payload (model declined to judge) | 0.059 | 0.010 |
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".