“Beyond being analysts of doom”: scientists on the frontlines of climate action
Bibliographic record
Abstract
What happens when scientists become activists? In this paper, we discuss the principles, commitments and experiences of Scientist Rebellion (SR), a movement of scientists, academics, and researchers committed to activism, advocacy and non-violent civil disobedience against the (in)actions of governments, corporations and other institutions, including academic ones. In sharing experiences from the frontlines of direct actions with SR along with the perspectives from individual scientists, coming from a variety of geographical locations, and a range of academic levels and disciplines, we reflect on the need to transgress the boundaries of a system of knowledge production and education that is effectively reproducing the very structures that have led us into climate and ecological crises. This article provides a reflective and critical engagement with Scientist Rebellion, drawing on a range of interviews with activists, as well as material from and about Scientist Rebellion. We conclude with a reflection on the relation between scientists and their institutions, as well as a mobilizing plea to the scientific community to take 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 | 0.025 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.034 | 0.069 |
| Scholarly communication | 0.021 | 0.027 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.012 | 0.022 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".