‘They don’t quite understand the importance of what we’re doing today’: the young people’s climate strikes as subaltern activism
Bibliographic record
Abstract
Abstract Background Youth-led movements like #FridaysforFuture and the school strikes for climate (henceforth referred to as the climate strikes) are leading calls for action on climate change worldwide. This paper reports on a thematic analysis of protest signs, and interviews with young climate strikers, at a climate strike in Manchester, UK, in 2019. Results This paper explores the ways in which dominant, adult-centred frameworks for conceptualizing young people’s environmental activism tend to obscure the complexities of the climate strike movement. In contrast, this study examines the complex political activism of climate strikers as a ‘subaltern group’, who take political action in a wider context of intersecting categories of oppression and marginalization – including youth as a category of marginalization – and in the historical context of environmental racism, the enduring legacies of colonialism, and global inequality during contemporary capitalism. Conclusions The article develops a theoretical model for future research, based on a model of two constraining frames that limit analysis of the climate strikes in particular and young people’s environmental activism in general. This paper contributes to a step change in methods for the study of this remarkable movement in a global context.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".