The Future of Divestment: Proliferations of Counter-Hegemonic and Post-Extractive Divestment Movements
Bibliographic record
Abstract
Fossil fuel divestment has quickly become the largest divestment campaign in history, drawing attention to the large discrepancy between national climate commitments and the continued support of the fossil fuel industry. Yet, fossil fuel production and emissions continue to escalate rapidly. Our question is: what's next for the divestment movement? We propose a conceptual framework that identifies two waves of divestment leadership in which public pressure campaigns move towards targeting the extractive economic structures and predatory behaviors that permit fossil fuel extraction, and unsustainable resource extraction more generally, to continue without limit. Building on the three waves model of divestment, we postulate that a fourth wave of fossil fuel divestment organizing has already begun, one that focuses on banks, insurers, and other financiers of fossil fuel projects. Further into the future, we envision a fifth wave of divestment campaigns, whereby divestment is used in climate and environmental activists' arsenal to target firms that engage in environmentally damaging and unjust behaviors such as destructive mining activities, overconsumption, predatory debt or arbitration processes, or Indigenous rights violations. While divestment is not a panacea and does not displace the work of existing post-extractive or climate justice campaigns, we argue that divestment is a powerful tool that can be used to complement and amplify the work of environmental justice activists in other contexts beyond fossil fuels. This paper offers actionable suggestions for current and future activists and frames divestment as a tactic that will proliferate within other environmental movements in the transition towards a post-growth economy.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".