Fundamental Assumptions in Narrative Analysis: Mapping the Field
Bibliographic record
Abstract
The richness of narrative analysis resides in its unruly openness, but points of reference are needed to tame the variety in the field. This article suggests that researchers should grapple with two fundamental questions when conducting narrative analysis. The first pertains to the status attributed to narrative: it is defined as the very fabric of human existence or as one representational device among others? Emphasizing one answer over the other means mobilizing different theories of representation and therefore, suggesting different articulations between "narrative" and "reality." The second question refers to the perspective developed on narrative: Is it defined mostly as the characteristic of an approach, an object of investigation or both? Different methodological implications are associated with that choice. The article claims that dominant trends in narrative analysis originate in the way researchers answer those two questions.
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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 | 0.053 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.011 | 0.055 |
| Scholarly communication | 0.027 | 0.037 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| 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".