Narrative Identity and the Redemptive Self: An Intellectual Autobiography, with Occasional Critique
Bibliographic record
Abstract
In this intellectual autobiography, I trace the development of the idea of narrative identity as manifest in personality and developmental psychology. As far as my own work in this area is concerned, the story begins in the early 1980s when my students and I struggled to understand the meaning of Erik Erikson’s concept of identity. Early work on a life-story model of identity aimed to situate the concept within the rapidly transforming field of personality psychology, first articulated as an alternative to the ascending conception of the Big Five traits. Eventually, I turned my attention to the redemptive life stories told by highly generative American adults, as my understanding of narrative identity came to be more fully contextualized in culture and history. While hundreds of nomothetic, hypothesis-testing studies of narrative identity have been conducted in the past two decades, the concept has also proven useful in the realm of psychobiography, as illustrated in my case studies of the redemptive life story constructed by the American President George W. Bush, and in my research into the strange case of President Donald J. Trump, whose most striking psychological attribute may be the near total absence of a narrative identity.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.034 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".