Community and religious involvement as contexts of identity change across late adolescence and emerging adulthood
Bibliographic record
Abstract
Latent growth curve modeling was used to describe longitudinal trends in community and religious involvement and Marcia’s (1966) four identity statuses (diffusion, foreclosure, moratorium, and achievement), as well as to assess relations between involvement and identity change. Cross-lagged regression models explored temporal ordering of relations between involvement and identity. The study involved 418 participants (Wave 1 M age = 17.44, SD = .81) over four occasions. Individuals on average showed decreases in community and religious involvement, identity diffusion, foreclosure, and moratorium, and no significant change in identity achievement. For community involvement, rates of change were related negatively to those for diffusion and positively to those for achievement. For religious involvement, rates of change correlated negatively with those for diffusion and moratorium, and positively with those for foreclosure. Cross-lagged models showed some effects in the expected direction (involvement to identity), as well as some reciprocal effects. All analyses were conducted for overall identity status as well as the three domains within each status (political, religious, and occupational). In short, the present study provides evidence for community and religious involvement as contexts facilitative of identity formation in late adolescence and emerging adulthood.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".