Bibliographic record
Abstract
Science, humanities and design might seem like unrelated fields. Yet, information designers, who unpack complex data involving real-world issues, can benefit from the ability to synthesize these seemingly disparate practices. To learn more integrated, humanistic approaches to data visualization, we might look to a time when science and the arts were less divided. The following chapter focuses on poet-scientist Johann Wolfgang von Goethe, the Romantic-era polymath. Goethe called his scientific method ‘tender empiricism’, a complementary practice to analytical empiricism. Goethe believed in portraying the same phenomena under subtle, changing conditions. While observing, collecting and visualizing, he also searched for what might be missing. A plant, for example, is not a collection of parts; it also portrays the process of growth even in static form. For Goethe, observational discoveries can change the inquiring mind. In contrast to data visualization practice today, which often focuses on summaries and abstract charts, Goethe believed that authentic, insightful truth dwells in real-world details. The second half of the chapter illustrates how Goethe’s ‘tender empiricism’ can be applied to design pedagogy. These case studies show how a Goethean ecological approach can be used to model a more ethical way of working with data.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.024 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".