Bibliographic record
Abstract
Climate change is an anthropogenic, geological issue that has earned much attention in the past few years. The issue has been put to the forefront by then 16-year-old Swedish political activist Greta Thunberg, who began protesting every Friday outside the Swedish parliament in August 2018 (Fraser & Westbrook, 2019) asking the government to reduce carbon emissions in accordance with the Paris Agreement. Her approach inspired many young people around the world. These protests gained momentum and by 2019 the Guardian reported that roughly 6 million people were participating in the global climate strikes (Taylor, Watts, et al. 2019). These protests continued into 2020, however, due to the global pandemic, protesters and activists had to find alternative ways to spread awareness. Images then became more important and also morphed into another kind of storytelling. Images are at the core of this research since images can have a profound effect on our memories, and digital media expanded the possibilities to document protests using images. The focus of this article is on digital storytelling and visual framing. This research qualitatively and quantitatively analyzes 1,394 images of the 2019 and 2020 youth climate strike. By collecting Google Images and using a framework that allows us to study narratives and image-making, we show how the pandemic changed the imagery of this movement moving protest tactics into more individualized, instead of collective, expressions. While news media are still dominant in posting protest images, their ability to control the framing of a protest event is undermined by other sources, including protesters themselves. This research highlights new tactics used by social movements to sustain their activity during the pandemic. Department: Sociology Faculty Mentor: Dr. Shelley Boulianne
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".