Pictorial Narrative Mapping as a Qualitative Analytic Technique
Bibliographic record
Abstract
Qualitative analysis is often a textual undertaking. However, it can be helpful to think about and represent study phenomena or narrative accounts in nontextual ways. In this article, we share our unique and artistic process in developing and employing pictorial narrative mapping as a qualitative analytic technique. We recast a nontextual, artistic–analytic technique by combining elements related to narrative mapping and narrative art. This technique involves aesthetic attunement to data and visual representation through pictorial design. We advanced this technique in the context of a narrative study about how arts-informed dissemination methods influence health-care practitioners’ delivery of care. We found that the Pictorial Narrative Mapping process prompted an aesthetic and imaginative experience in the analytic process of qualitative inquiry. As an analytic technique, Pictorial Narrative Mapping extends the inquiry process and enhances rigor through artistic means as well as iterative and critical dialogue. Additionally, pictorial narrative maps can provide a holistic account of the phenomenon under study and assist researchers to make meaning of nuances within complex narratives. As researchers consider employing Pictorial Narrative Mapping, we recommend that they draw upon this technique as a malleable script yielding to an organic process that emerges from both their own data and analytic discussions. We are further curious about its imaginative capacities in social and health science literature, its possibilities in other disciplinary contexts, and the prospects of what Maxine Greene refers to as becoming more wide awake—in our case, in future research analytic endeavors.
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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 | 0.113 | 0.130 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".