Datafying climate justice education: more-than-human learning and self-care in the algorithmic Anthropocene
Bibliographic record
Abstract
This paper examines the role of environmental sensor technologies in climate justice education. It brings together theories of self-knowledge and self-care with posthuman theories of learning to explore how climate justice education might account for its more-than-human digital entanglements. I specifically explore community-based climate justice education projects in which residents use wearable sensor technologies to track the air quality around them and learn about unjust distributions of smog and air pollution. I draw on Elizabeth de Freitas’s work on more-than-human worldly sensibility, which shows that learning about the environment is not limited to human perception. Environmental sensor technologies offer communities a way to engage with the burnout that follows from both deficits of knowledge and excesses of data in the Anthropocene. They thus become more-than-human participants in learning about conditions vital for life. I show that such projects produce speculative education practices in which learning through citizen science and data justice education is positioned as a form of self-care.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.056 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".