Empowering Teenage Girls to Save the Planet? Idealized Girlhood, Green Girl Power, and the ‘Girling of Climate Change’
Bibliographic record
Abstract
This dissertation explores idealized constructions of girlhood that have gained traction in global climate change politics over the last decade -a discourse that I refer to as the 'girling of climate change'.It undertakes a feminist discourse-theoretical analysis using anti-imperialist feminisms to explore two constructions of girlhood: 1. girls as 'objects of investment' where girls are depicted as tools for poverty alleviation and climate adaptation in the Global South; and 2. girls as 'iconic' where girl climate activists are seen as inspirational and aspirational symbols of hope for greener futures.This analysis reveals how -by emphasizing individual girls, their resilience, and their exceptionalism -this discourse governmentalizes girls' lives along highly neoliberal lines.Indeed, girls learn that they must be individually exceptional and save the world on their own before they are valued in global politics.Still, the dissertation argues that this discourse is not entirely hegemonic.By practicing attentive listening as proposed by feminist care ethics, this analysis illuminates how girls are creatively troubling its logics.Girls do not want to save the world alone; instead, girls remind us that the climate crisis requires structural reimaginings of capitalism and colonialism, as well as intergenerational and collective social movement 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.005 |
| 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".