Life's little (and big) lessons: Identity statuses and meaning-making in the turning point narratives of emerging adults.
Bibliographic record
Abstract
A longitudinal study examined relations between 2 approaches to identity development: the identity status model and the narrative life story model. Turning point narratives were collected from emerging adults at age 23 years. Identity statuses were collected at several points across adolescence and emerging adulthood, as were measures of generativity and optimism. Narratives were coded for the sophistication of meaning-making reported, the event type in the narrative, and the emotional tone of the narrative. Meaning-making was defined as connecting the turning point to some aspect of or understanding of oneself. Results showed that less sophisticated meaning was associated particularly with the less advanced diffusion and foreclosure statuses, and that more sophisticated meaning was associated with an overall identity maturity index. Meaning was also positively associated with generativity and optimism at age 23, with stories focused on mortality experiences, and with a redemptive story sequence. Meaning was negatively associated with achievement stories. Results are discussed in terms of the similarities and differences in the 2 approaches to identity development and the elaboration of meaning-making as an important component of narrative identity.
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 | 0.003 | 0.017 |
| 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 | 0.002 | 0.002 |
| Open science | 0.000 | 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".