Bibliographic record
Abstract
Serendipity, a trigger of exciting yet unexpected discoveries, is an important but comparatively neglected factor in information seeking, research, and ideation. We suggest that serendipity can be facilitated through visualization. To explore this, we introduce the Bohemian Bookshelf, which aims to support serendipitous discoveries in the context of digital book collections. The Bohemian Bookshelf consists of five interlinked visualizations each offering a unique overview of the collection. It aims at encouraging serendipity by (1) offering multiple visual access points to the collection, (2) highlighting adjacencies between books, (3) providing flexible visual pathways for exploring the collection, (4) enticing curiosity through abstract, metaphorical, and visually distinct representations of books, and (5) enabling a playful approach to information exploration. A deployment at a library revealed that visitors embraced this approach of utilizing visualization to support open-ended explorations and serendipitous discoveries. This encourages future explorations into promoting serendipity through information visualization.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.005 |
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".