Narrative Analysis: Demonstrating the Iterative Process for New Researchers
Bibliographic record
Abstract
This article demonstrates and describes an iterative process of narrative analysis for researchers who want to familiarise themselves with this methodology. The method draws on the six-step process of how to analyse a narrative, the four modes of reading a narrative and the three-sphere model of external context. The application of the method is demonstrated through describing the process of analysis of New Zealand school counsellors’ narratives of strengths-based counselling. Furthermore, this article posits that committing to a narrative analysis process of repeated and in-depth engagement with participants’ narrative data may facilitate a more robust and engaging research outcome than may otherwise have been achieved through more prescriptive methods of narrative analysis. Finally, this article highlights the use of story-map grids (tables) and models as visual aids to assist in the process of narrative analysis.
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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 | 0.159 | 0.161 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.017 | 0.033 |
| Scholarly communication | 0.019 | 0.023 |
| Open science | 0.005 | 0.025 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".