The Epistemological Status of Autobiographical Accounts or How to Treat Personal Narratives
Bibliographic record
Abstract
The purpose of this article is to show how different understandings of truth-correspondence, consensual, subjective, and fictional, as well as the truth of a particular interest group, affect the treatment and interpretation of autobiographical accounts. At the same time, these accounts are understood here as personal narratives told from the perspective of the narrator's personal experience, presenting events in a specific temporal and causal order. An autobiographical account captures a person's life in terms of a story about himself, but also about those whom the story depicts. The article opens with a discussion of the epistemological status of autobiographical data most often cited in the literature, which amounts to treating narratives as objective events or merely subjective accounts of them. Moving beyond this dichotomous division is presented in the remainder of the article, which discusses the meaning of truth in the four views of social reality and how this affects the treatment of life stories. The analytical areas identified provide an understanding of how to treat empirical data and what cognitive results should be expected if one adopts a given understanding of truth.
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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 | 0.032 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.074 |
| Scholarly communication | 0.016 | 0.022 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".