Bibliographic record
Abstract
Recently, a number of studies have drawn attention to the narrative fabric of autobiographical identity construction. In this process, time plays a pivotal role, both as a structure and object of construction. In telling our lives, we deal not only with the classical time modalities of past, present, and future, but also with the different temporal orders of natural, cultural, and individual processes. We find all forms of linguistic constructions of time, such as tense systems, tropes, anachronies, and the use of specific narrative genres. In this paper, I shall argue that in the process of autobiographical identity construction a particular synthesis of cultural and individual orders of time takes place. The result is autobiographical time, the time of one’s life. For this synthesis the form of narrative is not only the most adequate form, it is the only form in which this most complex mode of human time construction can exist at all. Discussing various case studies, I shall distinguish six different narrative models of autobiographical time: the linear, circular, cyclical, spiral, static, and fragmentary model. To study how people make use of these models in their autobiographical narratives is to investigate how we become immersed into the fabric of culture and, at the same time, express our unique individuality.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".