Sowing seeds for future generations
Bibliographic record
Abstract
In this longitudinal study, we examined the relationship between the trajectory of generative concern measured at ages 23, 26 and 32 and environmental narrative identity at age 32. Canadian participants completed a questionnaire on generative concern at ages 23, 26 and 32 and were then interviewed about their personal experiences with the environment at age 32 ( N = 112). Narratives were coded by independent raters for meaning, vividness and impact, with higher levels indicating a more salient environmental narrative identity. Latent growth models revealed significant individual variability in the trajectories of generative concern from ages 23 to 32. This variability was associated with the salience of environmental narrative identity at age 32 through two different developmental processes: (1) having a higher level of generative concern at age 23 predicted a more salient environmental narrative identity at age 32; and (2) those who developed higher levels of generative concern during the course of emerging adulthood (from ages 23 to 32) also appeared to display a more salient environmental narrative identity at age 32. Implications of these findings are also discussed.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".