Environmental and Climate Justice in Computing
Bibliographic record
Abstract
While climate change has been a longstanding concern of HCI and CSCW communities, this scholarship has rarely drawn attention to the well-documented pattern of minoritized and marginalized communities unfairly carrying the brunt of environmental burdens. Through this one-day remote workshop, we plan to critically extend how CSCW can support climate action by focusing on two social movements, environmental and climate justice, both of which aim to reduce environmental degradation and pursue sustainable communities without doing so at the expense of others. In this workshop, we aim to identify how CSCW and datafication have helped to uphold environmental or climate justice commitments or has been complicit in producing or maintaining environmental harms. We also plan to discuss and identify a CSCW research agenda addressing how to support climate justice principles and processes in designing technologies and systems. We hope that this workshop will help to initiate and foster a longer-term relationship with researchers, activists and practitioners who are engaging with or interested in climate justice in computing.
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.025 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".