Reparative accumulation? Financial risk and investment across socio-environmental crises
Bibliographic record
Abstract
With the growing global recognition that environmental and social crises are pushing systems of social and ecological reproduction to their breaking points, governments, philanthropists, and the private sector are proposing a variety of strategies that aim to shift the social and environmental role of finance capital from an extractive process to a reparative one. A frequent refrain is that only finance capital promises the scale of investment necessary to address Earth’s complex social and environmental problems, and that trillions of private investment dollars wait in the wings ready to mobilize for the right kinds of projects. A hallmark of these approaches is their promise of “triple bottom line” outcomes, with social, environmental, and financial benefits—what the industry refers to as “responsible investing.” This symposium interrogates the political dynamics and financial mechanisms underlying ongoing experiments in so-called responsible finance, including various forms of impact investing and financial “solutionism” to social and environmental problems. We develop the term “reparative accumulation” to conceptualize the divergent forms and continuities in how these new financial devices function across sectors, what types of futures the industry is attempting to create, the effects on socionatures, and what resistance might look like both within and outside these systems.
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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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".