Environmentalism as a Context for Expressing Identity and Generativity: Patterns Among Activists and Uninvolved Youth and Midlife Adults
Bibliographic record
Abstract
Previous qualitative studies have identified themes of generativity and identity development in the interviews of environmental activists (Chan, 2009; Horwitz, 1996), suggesting their importance as motives for environmental behavior. The purpose of our study was to extend this work by identifying positive relationships between identity maturity, generativity, and environmentalism using quantitative methodologies. To explore these relationships, we designed quasi-experimental and correlational studies. We recruited 54 environmental activists and 56 comparison individuals, half of whom were youth (mean age = 22 years) and the other half midlife adults (mean age = 43 years). Sixty-three percent of our sample was female. Participants completed several environmental, generativity, and identity questionnaires. We found that activists and comparison individuals differed on the identity maturity, generativity, and environmental measures overall. Further, greater identity maturity and generativity were associated with higher environmental engagement. And generativity was found to mediate the relation between identity maturity and environmentalism. Our findings suggest that engaging in generative behaviors may be an important part of the process in forming an environmental identity and engaging in environmental actions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".