Slow justice: a framework for tracing diffusion and legacies of resistance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it