"Change is Coming": Imagined Futures, Optimism and Pessimism Among Youth Climate Protesters
Bibliographic record
Abstract
In recent years, two unrelated developments have opened up new opportunities for examining how young people relate to climate change and participate in climate politics. First, there is a fast-growing literature in sociology and youth studies concerned with the roles of imagined futures in social action. Second, and more recent, is an explosion of youth-based climate activism, particularly the Fridays For Future movement. In this paper, I draw from in-depth interviews with participants in the Fridays For Future protests in London in Spring 2019, arguing that in this case of youth mobilization, protesters relied on shared, overarching narratives about the future of climate change, albeit ones that allow room for some divergence in opinion. In particular, I examine how regular involvement in the movement influenced participants’ imagined futures. Drawing from studies of similar issues by Kleres and Wettergren (2017) and Threadgold (2012), and from the phenomenological concept of “orientation” (Ahmed, 2005; Carabelli and Lyon, 2016), I argue that regular and repeated participation in climate activism engenders optimism among youth. This opens new ways of thinking about the relationship between political action and young people’s anticipations of climate change, with implications for scholarship of imagined futures, youth politics and climate politics.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".