Financing Reparative Climate Infrastructures: Capital Switching, Repair, and Decommodification
Bibliographic record
Abstract
Abstract Despite geographical critiques of the financialisation of climate governance, the realities of deteriorating environmental conditions, entrenched market logics, and the concentration of capital in the hands of financiers demand new strategies to contend with climate finance. We envision routes to better futures by surveying “financialised” responses to climate catastrophe that might be harnessed towards more reparative and decommodified ends. We combine ideas of “repair” and “capital switching” to evaluate financial tools for “reparative climate infrastructures” in five cases centred on energy, land, and water in the United States, Australia, Indonesia, and Brazil. Through these cases, we identify three key themes—governance, scale, and the state—that illuminate the socioecological, material, and political dimensions of reparative capital switching. The cases are each hopeful and cautionary. Together they offer a window into the contested terrain of climate finance in the present and highlight the need for critical attention to its strategic possibilities.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".