A Micro-Phenomenological Lens for Evaluating Narrative Visualization
Bibliographic record
Abstract
Narrative visualizations engage audience in data stories, evoking emotions by using narrative patterns, rhetoric, visual design, and content among other strategies. How these elements combine to influence user experiences is complex and difficult to measure using empirical methods. This is partly due to the fact that narrative visualizations influence audiences affectively and implicitly [1]-[3]. Evaluations of narrative visualizations that aim to better understand these mechanisms should capture this rich complexity by focusing on gathering descriptions of lived experience. Micro-phenomenology, a rigorous set of methods developed for soliciting descriptions of experiences, has empirically been shown to improve recollection of otherwise implicit aspects of experience [4]. Building on work using micro-phenomenological interviews to evaluate static visualizations [5], we apply these methods to interactive narrative visualizations. We conducted a small study to explore the potential of these methods in this context. Our findings reveal how narrative patterns and designs influence affective states, how they support various forms of exploratory analysis, and how they can facilitate or hinder non-analytical reflection such as the imagining of stories described within visualizations. These types of insights can inform future designs and help researchers understand how techniques employed in narrative visualizations influence users in specific and often implicit ways.
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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 | 0.022 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".