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 written publication in ACM interactions magazine.
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 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 | 0.043 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.023 | 0.040 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".