Thinking algorithmically: The making of hegemonic knowledge in climate governance
Bibliographic record
Abstract
Algorithms – instructions for acting on data, executed by code – are increasingly being enrolled into climate policy governance via the prediction of policy outcomes, the evaluation of climate mitigation and adaptation strategies, and the design of practitioner actions. Yet the political implications of these technological changes in environmental governance are only just beginning to be theorised. In this paper, we examine one particular facet of this emerging politics: the relationship between thinking algorithmically and hegemonic power. Drawing from Laclau and Mouffe’s theorisation of hegemony we argue that algorithmic forms of reasoning lend themselves towards producing hegemonising knowledge regimes, with important implications for a democratic politics of climate change. Recognising that algorithms stand for wider socio‐technical assemblages that structure and create knowledge, we call for greater attention to the reliance on algorithms within climate governance – less for the algorithms themselves than for their particular epistemic commitments that create algorithmic ways of thinking, with associated claims to power. Through a critical review of scholarship at the intersection of critical digital studies and environmental governance, we first identify three key epistemic commitments involved in thinking algorithmically: induction, abstraction, and optimisation. We then examine the correspondence between these key features of algorithmic thinking and the conditions that Laclau and Mouffe propose form the grounds for hegemony: objectivity, universality, and necessity. Better understanding what “thinking algorithmically” entails, and the forms of knowing and acting that it affords and excludes, is vital, we argue, to begin naming the political implications and transformative potential of new forms of climate governance.
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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 | 0.019 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.092 |
| Scholarly communication | 0.013 | 0.023 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".