Discursive Narrative Analysis: A Study of Online Autobiographical Accounts of Self-Injury
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.
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 | 0.010 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".