Environmental and Climate Justice in Computing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it