Climate Data Practices: A Research Approach for HCI and Climate Justice
Bibliographic record
Abstract
This article introduces climate data practices as a conceptual lens for HCI research and design toward social and environmental justice. We offer a working definition of this approach, which we locate at the intersection of critical data studies and practice theory. Drawing from a collaborative and multi-sited study of activists, city staff, and non-profit organizations, we present six examples of climate data practices as a means of illustrating the approach as well as the diversity of issues that it may usefully surface. Through these examples, we demonstrate that a data practices approach foregrounds the local, relational, and plural qualities of climate data. Finally, we connect data practices to two related concepts—scenes and infrastructures—which together offer a framework to guide critical interrogation of the social and political life of data, and support the design of data practices that serve broader goals of social and environmental justice.
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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 | 0.052 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.010 | 0.085 |
| Scholarly communication | 0.033 | 0.036 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.008 | 0.011 |
| 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".