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.
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.
How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".