Discursive Narrative Analysis: A Study of Online Autobiographical Accounts of Self-Injury
Why this work is in the frame
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Bibliographic record
Abstract
This article offers an innovation in narrative analysis afforded by incorporating analytic concepts from discourse analysis. We share some examples from our study of online autobiographical accounts of non - suicidal self - injury (NSSI) to illustrate the various aspects of a discursive narrative approach to research. We show how the participants construct events and experiences as sequentially linked and temporarily related using a range of discursive practices and devices, including producing contrasting descriptions of emotional states, using figurative language, vivid or vague descriptions, and extreme case formulations. The specific way in which experience was constituted as sequentially and causally linked allows narrators to attribute relief from suffering to NSSI and to present NSSI as a reasonable and justifiable behavior to those who may read these autobiographies. This study offers insight into what may be missed when interpretation is focused solely on the content or broad structural elements of stories, as in much narrative analysis, and suggests the critical role of narrators’ social or interactive orientation and their reliance on the micro - details of language in the construction of stories. Methodological and theoretical implications are discussed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it