Pathways to environmental activism in four countries: social media, environmental concern, and political efficacy
Bibliographic record
Abstract
In 2018-9, millions of youth participated in climate-related marches across the globe. This activism reflects youth’s distinctive form of political participation: cause-oriented, expressive, and networked. However, the pathway between environmental concern and environmental activism is complicated in some contexts and for some citizens. This article uses survey data from four countries (Canada, France, the United Kingdom, the United States) gathered in autumn 2019. We focus on the environmental activism of youth and young adults (aged 18–33 years, n = 1574). We find the role of social media is consistent and strong for all environmental activities in all countries; the role of political efficacy depends on activity and country but has a positive role in environmental activism; and environmental concern is a positive and significant correlate of boycotting and signing petitions but a weak predictor of participating in environmental marches. The relationship between environmental concern and environmental marches is distinctive in the United Kingdom. Overall, we find that concern about a social cause does not automatically translate into increased activism related to that cause. Moreover, online social networks, political efficacy, and political context are critical to understanding this mobilization process.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".