Animated Analysis: Drawing Deeper Analytical Insights From Qualitative Data
Bibliographic record
Abstract
While participant-created drawings in arts-based health research, used as a process of producing knowledge are well known, similar approaches with researcher-created drawings are less common. This article describes the journey of how researcher-created drawings as an arts-based analytical approach helped a novice researcher to draw deeper into the interpretive process. Emerging from a positivist paradigm, a proceduralist understanding of the qualitative methods was readily grasped by this researcher, but developing reflexivity and deep analytical insights required facilitation. An overarching interpretivist qualitative approach that aligns with Gadamerian philosophical hermeneutics was used to analyze participant observation data (field notes, researcher-created drawings) of decision-making encounters between families of youth with brachial plexus birth injuries and the health care team in the clinic setting. Drawing acted as an analytical catalyst such that the task of creating a visual product helped this researcher to look beyond descriptive, factual and procedural information in participant observation data. Drawing created spontaneity that fostered freedom to interpret, while hermeneutic reflection created self-dialogue about understandings that arose from all data sources. Reflexivity was cultivated through deliberating on the creative process that resulted in choices of composition and content to represent the observed sessions. Drawing can help qualitative researchers animate their analyses through a visible and accountable method of constructing new knowledge.
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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 | 0.093 | 0.113 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".