Granny Solidarity: Understanding Age and Generational Dynamics in Climate Justice Movements
Bibliographic record
Abstract
Since the 2018 Intergovernmental Panel on Climate Change (IPCC) report, a global shift in consciousness has taken place around the urgency of the Earth’s climate crisis. Amidst growing panic, teenagers are emerging as key leaders and mobilizers, demanding intergenerational justice and immediate action. They are, however, often depicted as lone revolutionaries or as pawns of adult organizations. These representations obscure the complex and important ways in which climate justice movements are operating, and particularly the ways in which dynamics of age intersect with other axes of power within solidarity efforts in specific contexts. This article explores these dynamics, building on analyses of intersectional and intergenerational solidarity practices. Specifically, it delves into detailed analysis of how the Seattle group of the Raging Grannies, a network of older activists, engaged in Seattle’s ShellNo Action Coalition, mobilizing their age, whiteness, and gender to support racialized and youth activists involved in the coalition, and thus to block Shell Oil’s rigs from travelling through the Seattle harbour en route to the Arctic. Drawing from a pivotal group discussion between Grannies and other coalition members, as well as participant observation and media analysis, it examines the Grannies’ practices of solidarity during frontline protests and well beyond. The article thus offers an analysis of solidarity that is both intergenerational and intersectional in approach, while contributing to ongoing work to extend understandings of the temporal, spatial, cognitive, and relational dimensions of solidarity praxis.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| 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".