One Picture to Study One Thousand Words: Visualization for Qualitative Research in the Age of Digitalization
Bibliographic record
Abstract
In this article, we argue for further advancing qualitative research methods by creating tools to investigate digital traces of digital phenomena. We specifically focus on large-scale textual data sets and show how interactive visualization can be used to augment qualitative researchers’ capabilities to theorize from trace data. We ground our approach on prior work in sensemaking, visual analytics, and interactive visualization (Munzner 2014; Russell et al. 1993) and show how tasks enabled by visualization systems can be synergistically integrated with the qualitative research process. Finally, we apply these principles with several open-source text mining and interactive visualization systems. We hope that this articlestimulates further interest and provides specific guidelines for developing and expanding the repertoire of open-source systems for qualitative research.
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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 | 0.115 | 0.199 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.010 | 0.019 |
| Scholarly communication | 0.018 | 0.026 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.015 | 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".