Slow justice: a framework for tracing diffusion and legacies of resistance
Bibliographic record
Abstract
Efforts to advance environmental justice are often halting and uneven. How can we identify the longer-term significance of protests that seem to have failed? In this article, we turn to work on environmental injustice to examine the consequences of environmental justice movements over time and across space. We draw on the scholarship of Rob Nixon on ‘slow violence’: rather than the spectacular, visceral, and immediate violence of war, he argues that environmental degradation is a violence that operates in cumulative, slow-moving, accretive, and multi-causal ways. Borrowing – and flipping – Nixon’s conceptualization, we suggest that a parallel process of ‘slow justice’ is taking place. As with environmental damage, mobilization for environmental justice can have consequences that are dispersed in time and place, occur in non-linear forms, and operate at multiple scales. To track the pathways through which slow justice emerges, we develop a three-part typology of social movement connectivity. Using the categories of people, projects, and processes, we identify the geographically and temporally distanced social, material, and governance legacies of moments of resistance. Through a case study of mobilization against fossil fuel infrastructure in the Mackenzie Valley in northern Canada in the 1970s, we use the typology to trace how this moment of mobilization shaped other efforts of environmental justice organizing, including for campaigns in different regions and on different issue-areas. We argue that slow justice can reframe how we understand the outcomes of social mobilization projects, making visible the often obscure, indirect, and long-term accrued benefits of environmental justice work.
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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 | 0.022 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.022 | 0.012 |
| Science and technology studies | 0.010 | 0.051 |
| Scholarly communication | 0.014 | 0.026 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".