Bibliographic record
Abstract
In the literature on autobiographical narrative, self, and identity construction, many researchers have taken narrative coherence as an important feature that reflects and shapes identity and sense of self. Commonly, this feature is defined and assessed in isolation, as if at stake were an autonomous text. We argue this approach is too narrow to represent things as complex as narrative, self, and brain. We explain this argument in discussing narratives by individuals with serious neuropsychological challenges: people who, due to illness or disability, cannot fully rely on their neurocognitive and narrative resources for their identity construction. We offer a broader view of the issue of coherence in autobiographical narrative that goes beyond a decontextualized concept of narrative, especially, by including (i) the intersubjective context in which stories are told, (ii) the larger autobiographical context of their narrator, and (iii) the wider socio-cultural context in which narratives and narrators are situated. Using narrative excerpts from adults with acquired brain injuries and neurocognitive disabilities, we point out how what is seen as (narrative) coherence of one’s brain, mind, and self changes when these contexts are taken into account.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.042 | 0.009 |
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".