Climate action among Generation Z: The association between ingroup identification, collective efficacy, and collective action intentions and behaviour
Bibliographic record
Abstract
The majority of today’s emerging adults view climate change as the defining challenge of their generation (Amnesty International, 2019). Young people’s climate concern has translated to unprecedented collective climate action, such as the youth climate strikes of 2019. However, young people and their relevant social identities are underrepresented in research on collective climate action. Following the social identity model of pro-environmental action (Fritsche et al., 2018), the current study assesses the extent to which emerging adults identify with Generation Z, or Gen-Z, as a relevant ingroup. In a Prolific survey of 296 participants aged 18-24 and currently living in Canada, I examined young people’s Gen-Z ingroup identification, perceived collective efficacy of Gen-Z, and three collective action outcomes: intentions to follow youth climate groups on social media, intentions to engage in future collective climate action, and participation in sending an advocacy message to the B.C. Minister of Environment and Climate Change Strategy. I hypothesized that the interaction of ingroup identification and collective efficacy would predict collective climate action outcomes above and beyond the influence of each construct individually. This hypothesis was not supported. While Gen-Z ingroup identification and perceived collective efficacy each predicted intentions to follow youth climate groups on social media and intentions to engage in future collective action, the interaction term added no explanatory power to the models. Neither Gen-Z ingroup identification nor collective efficacy predicted participation in the advocacy message behaviour. These findings underscore the importance of systematically investigating broad social identities in the field of collective climate action, which has predominantly focused on specific environmentalist groups. The current study also highlights the need for further investigation of predictors of behavioural outcomes.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".