Autonomy, Affect, and Reframing: Unpacking the Data Practices of Grassroots Climate Justice Activists
Bibliographic record
Abstract
Though not often considered primary users or creators of climate change data, grassroots climate activism is increasingly data driven. This study looks at the ways in which grassroots climate justice groups engage with data to further their goals. The authors employ a qualitative research design rooted in reflexive thematic analysis for this project, with methods including a series of semi-structured interviews and an analysis of digital content produced by grassroots climate justice groups. We identify five distinct functions of data which support the work of local climate activists. These functions highlight how engagement with data in this context is intertwined with autonomy, affect and the reframing of what counts as climate data and expertise. This study contributes to further understanding of the relationship between grassroots climate justice groups and data practices, highlights barriers groups face with data engagement, and offers recommendations for HCI to further support local climate action.
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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 | 0.036 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.029 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".