Datafication of Climate Change: From Prediction to Participation
Bibliographic record
Abstract
How is the climate crisis mediated by data?In this one-day hybrid workshop, we will gather an interdisciplinary group of critical computing scholars and practitioners examining and practising the different ways that climate change is becoming datafied.The datafication of climate change refers to the collection, analysis, and representation of data to inform decision-making around strategies for the prevention, mitigation, or adaptation to the impacts of climate change.While increased use of data promises to keep powerful actors accountable for their actions and enable targeted decision making necessary for policy analysis, there are challenges regarding how and what kinds of data are being used and for whom.Furthermore, the increased data usage also entails environmental impacts, such as the rising energy consumption, which may contradict the intended goals behind datafication in the first place.In this workshop, we aim to map the climate data pipeline, based on presentations by participants on their current research in relation to the data lifecycle (e.g.cleaning, storing, analysis, scraping, etc.) with a tentative output of a
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 | 0.000 | 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) | 0.001 | 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".