Nurturing Activist Love: Strategies for Cultivating and Sustaining Youth Climate Justice Organizing
Bibliographic record
Abstract
In September 2019, over 6 million people worldwide took to the streets for a series of youth-led climate strikes. Inspired by the “Fridays for Future” movement, this moment saw students, teachers, grassroots groups, unions, environmental nonprofits, and social justice organizations converge in the largest global mobilization for climate justice to date. Such “moments of the whirlwind” (Engler & Engler, 2016) can help propel social movement organizing to new heights, but they are also ephemeral public manifestations of unity that elide the much more complex and contested work of making a movement (Bravo et al., 2023). Historically, youth have played leading roles in driving social transformation. Notable examples include the Student Nonviolent Coordinating Committee during the civil rights movement in the United States (Payne, 1995), pro-democracy uprisings during the Arab Spring (Mulderig, 2013), and the leadership of the Palestinian Youth Movement. Youth leading movements for climate justice are no exception, and the 2019 global climate strikes are a prominent example of the ability of youth to mobilize for climate action in numbers not previously seen through more established nongovernmental organizations and adult-led policy channels.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".